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With the prosperity of wireless and mobile communications, the allocation of wireless resources, e.g., spectrum channels, femtocell access permissions, and resource blocks of D2D connections, has become a matter of great concern, which leads to the emergence and popularity of electronic auction. However, the lack of privacy protection and fairness guarantee in the existing market has posed great obstacles to user participation in electronic auctions. Without privacy protection, users will be worried about the unauthorized exposure of their commercial secrets, which may hurt their payoffs in the long run. Without fairness guarantee, selfish/malicious users may prematurely abort the auction to avoid unsatisfactory payment, thus causing loss to honest users and wasting system resources. Furthermore, the lack of fairness guarantee will incur an exchange dilemma, where mutually distrusting parties may refuse to initiate the goods/money exchange, hindering the market-clearing of auctions. Although extensive efforts have been made to develop privacy-preserving auction mechanisms for wireless markets, the fairness issue, to the best of our knowledge, has never been taken into account in the context of auctions. Moreover, a truly practical framework that can be applied to most mainstream auction formats is also missing in the literature. In this paper, we present the first general framework, named SAFE, for representative auctions in (but not limited to) wireless markets. SAFE achieves a wide range of security goals with much higher efficiency than existing solutions. Furthermore, SAFE ensures auction fairness in a trust-free manner by developing a full set of carefully-designed modular protocols that can be easily adapted to any auction format. We implement the SAFE framework over a simulated Ethereum network. Extensive experimental results confirm that SAFE can indeed achieve high economic efficiency in terms of social welfare and participation satisfaction at very low computation and communication overheads.

As one of the biggest contributors to road accidents and fatalities, drink driving is worthy of significant research attention. However, most existing systems on detecting or preventing drink driving either require special hardware or require much effort from the user, making these systems inapplicable to continuous drink driving monitoring in a real driving environment. In this paper, we present DetectDUI, a contactless, non-invasive, real-time system that yields a relatively highly accurate drink driving monitoring by combining vital signs (heart rate and respiration rate) extracted from in-car WiFi system and driver's psychomotor coordination through steering wheel operations. The framework consists of a series of signal processing algorithms for extracting clean and informative vital signs and psychomotor coordination, and integrate the two data streams using a self-attention convolutional neural network (i.e., C-Attention). In safe laboratory experiments with 15 participants, DetectDUI achieves drink driving detection accuracy of 96.6% and BAC predictions with an average mean error of 2 ~ 5mg/dl. These promising results provide a highly encouraging case for continued development.

Choosing the best cloud configuration for large-scale data analytics jobs deployed in the cloud can substantially improve their performance and reduce costs. However, current cloud providers offer a wide variety of instance types and customized cluster sizes, making it both time-consuming and costly to pinpoint the optimal cloud configuration. This article presents the design, implementation, and evaluation of Silhouette , a cloud configuration selection framework based on performance models for various large-scale analytics jobs with minimal training overhead. The essence of Silhouette is to build performance prediction models with carefully selected small-scale experiments on small subsets of input data to estimate the performance with entire input data on larger cluster sizes. To reduce the training time and cost, Silhouette incorporates new statistical techniques to select those experiments that yield the best possible information for performance prediction. Moreover, we develop a novel model transformer to convert a prediction model built on one instance type to a different instance type with only one extra experiment, which significantly reduces the training overhead. We evaluate Silhouette with an extensive array of large-scale data analytics jobs on Amazon EC2. Our experimental results have shown convincing evidence that Silhouette is effective in optimizing cloud configuration while saving both training time and costs compared with existing solutions.

The time and monetary costs of training sophisticated deep neural networks are exorbitant, which motivates resource-limited users to outsource the training process to the cloud. Concerning that an untrustworthy cloud service provider may inject backdoors to the returned model, the user can leverage state-of-the-art defense strategies to examine the model. In this paper, we aim to develop robust backdoor attacks (named RobNet) that can evade existing defense strategies from the standpoint of malicious cloud providers. The key rationale is to diversify the triggers and strengthen the model structure so that the backdoor is hard to be detected or removed. To attain this objective, we refine the trigger generation algorithm by selecting the neuron(s) with large weights and activations and then computing the triggers via gradient descent to maximize the value of the selected neuron(s). In stark contrast to existing works that fix the trigger location, we design a multi-location patching method to make the model less sensitive to mild displacement of triggers in real attacks. Furthermore, we extend the attack space by proposing multi-trigger backdoor attacks that can misclassify inputs with different triggers into the same or different target label(s). We evaluate the performance of RobNet on MNIST, GTSRB, and CIFAR-10 datasets, against four representative defense strategies Pruning, NeuralCleanse, Strip, and ABS. The comparison with two state-of-the-art baselines BadNets and Hidden Backdoors demonstrates that RobNet achieves higher attack success rate and is more resistant to potential defenses.

Following the correct stroke order while writing Chinese characters composed of strokes plays an important role in handwriting efficiency and quality, especially for early education. Most existing systems use image processing techniques for character and stroke order recognition, which is sensitive to lighting conditions. In this paper, we present the design, implementation and evaluation of SmartSO , which utilizes the inertial sensors of an off-the-shelf smartwatch for Chinese character and stroke order recognition. SmartSo first identifies the Chinese character written by the user, based on which SmartSo decides whether the stroke order is written correctly to help improve users' writing behavior. The biggest challenge for stroke order recognition is that some Chinese characters have repeated strokes (strokes of the same type), e.g., ‘本’ with two same horizontal strokes, and it is challenging to differentiate the writing order of such strokes given only the detected stroke composition (number and type of strokes). To mitigate this problem, we further analyze the hand movement between two adjacent strokes (referred to as direction motion) and propose a novel algorithm to recognize stroke order based on direction motion information. Finally, we build a fully functional prototype of SmartSO , and extensive experiments confirm its effectiveness and robustness.

Vehicular ad hoc networks ( VANETs ) and the services they support are an essential part of intelligent transportation. Through physical technologies, applications, protocols, and standards, they help to ensure traffic moves efficiently and vehicles operate safely. This article surveys the current state of play in VANETs development. The summarized and classified include the key technologies critical to the field, the resource-management and safety applications needed for smooth operations, the communications and data transmission protocols that support networking, and the theoretical and environmental constructs underpinning research and development, such as graph neural networks and the Internet of Things. Additionally, we identify and discuss several challenges facing VANETs, including poor safety, poor reliability, non-uniform standards, and low intelligence levels. Finally, we touch on hot technologies and techniques, such as reinforcement learning and 5G communications, to provide an outlook for the future of intelligent transportation systems.

With the widespread application of cloud storage, ensuring the integrity of user outsourced data catches more and more attention. To remotely check the integrity of cloud storage, plenty of protocols have been proposed, implemented by checking the equation constructed by the aggregated blocks, tags, and indices. However, the verifier only has the knowledge of the indices of the audited blocks and tags, which thus requires the cloud to store both data blocks and tags for integrity verification. In this article, we present a compressive secure cloud storage protocol inspired by Goldreich-Goldwasser-Halevi (GGH) cryptosystem. Since the aggregated blocks can be reconstructed from the aggregated tags without the help of data indices, the cloud can only store data tags for providing the verifiable integrity proof. In this way, communication and storage costs can be hugely reduced and user private information can be hidden from the cloud. Furthermore, the proposed protocol only contains a few basic algebraic operations, making it highly efficient. We also provide formal security proof of the proposed protocol regarding forge, replay and replace attacks. In addition, we explore a new technique to support data dynamics. Furthermore, we establish a generic framework of compressive secure cloud storage protocols. Finally, we provide the theoretical analysis and experimental results, which further validate the effectiveness of the proposed protocol.

Pattern lock is an essential authentication method on mobile devices. Recent works on cracking pattern locks either require additional network facilities (e.g., WiFi hotspots) or suffer from strict constraints (e.g., physical closeness to the victim and good lighting). Being too susceptible to environment settings, these attacks are less effective in practice and cannot scale to a large number of users. To address these concerns, in this paper, we propose PatternListener+, a practical attack on pattern locks using the speakers and microphones on mobile devices. The speaker plays inaudible acoustic signals, which are reflected by the fingertip when the victim is drawing the pattern, and then recorded by the microphone. The recorded acoustic signals contain rich information of the fingertip motion that can be leveraged to infer the pattern. We carefully design a series of algorithms to eliminate the dynamic and static interferences, segment acoustic signals into fragments corresponding to all pattern lines, and recover each line composed of the pattern according to the signals. Finally, we recover the candidate pattern by mapping all line candidates into grid patterns with a tree structure. We implement a PatternListener+ prototype using off-the-shelf smartphones, and extensive experiments confirm the effectiveness and robustness of PatternListener+. The attack success rate is over 90 percent on 120 patterns in five attempts.

Traditional health insurance schemes make contracts with periodic premiums according to the health conditions of customers. Insurance companies are thus seeking an incentive for a healthy lifestyle of customers. Nowadays, more and more people want to gather metrics associated with physical activity. High quality and quantity information introduced by wearable devices may indicate the potential risks of acute conditions. Therefore, wearable technology brings new opportunities for insurance companies to change the interaction way with customers and to improve risk management for users. In this article, we investigate the problem of incentive mechanism design for the insurance market boosted by wearable technology. Designing an effective incentive mechanism is challenging since both the insurer and the users are selfish and rational parties who try to maximize their own utility. There exists an information asymmetry, i.e., the insurer is unaware of the intrinsic physical fitness of each user ( type of users). Inspired by the contract theory, we design a series of optimal insurance contracts with different premium discounts and recommended exercise levels targeting different types of users. We first analyze the scenario where the insurer knows the type of each individual, then move to the more practical case where the insurer has incomplete information of user types. We theoretically prove that the proposed contracts satisfy individual rationality and incentive compatibility , which enables the insurer to achieve utility maximization and each type of user to choose the most appropriate contract. We have conducted extensive simulations to provide insight into the performance under different scenarios. The numerical results verify that our contract-based insurance incentive mechanism maximizes insurer’s utility and motivates users to exercise more.

Crowdsourcing has become a popular paradigm to leverage the collective intelligence of massive crowd workers to perform certain tasks in a cost-effective way. Task assignment is an essential issue in crowdsourcing platforms owing to heterogeneous tasks and work skills. In this article, we focus on assigning workers with diversified skill levels to crowdsourcing tasks with different quality requirements and budget constraints. Task assignment is fundamentally a many-to-one matching problem, where one task is allocated to multiple users who can meet the minimum quality requirement of the task within the limited budget. While most existing works try to maximize the utility of the crowdsourcing platform, we take into account the individual preferences of crowdsourcers and workers toward each other to ensure the stability of task assignment results. In this article, we propose task assignment mechanisms that can guarantee stable outcomes for the many-to-one matching problem with lower and upper bounds (i.e., quality requirement and budget constraint) in regard to heterogeneous worker skill levels. Extensive simulation results show that the proposed algorithms can greatly improve the success ratio of task accomplishment and worker happiness compared with existing algorithms.

Machine learning models have achieved state-of-the-art performance in various fields, from image classification to speech recognition. However, such models are trained with a large amount of sensitive training data, and are typically computationally expensive to build. As a result, many cloud providers (e.g., Google) have launched machine-learning-as-a-service, which helps clients benefit from the sophisticated cloud-based machine learning models via accessing public APIs. Such a business paradigm significantly expedites and simplifies the development circles. Unfortunately, the commercial value of such cloud-based machine learning models motivates attackers to conduct model extraction attacks for free use or as a springboard to conduct other attacks (e.g., craft adversarial examples in black-box settings). In this article, we conduct a thorough investigation of existing approaches to model extraction attacks and defenses on cloud-based models. We classify the state-of-the-art attack schemes into two categories based on whether the attacker aims to steal the property (i.e., parameters, hyperparameters, and architecture) or the functionality of the model. We also categorize defending schemes into two groups based on whether the scheme relies on output disturbance or query observation. We not only present a detailed survey of each method, but also demonstrate the comparison of both attack and defense approaches via experiments. We highlight several future directions in both model extraction attacks and its defenses, which shed light on possible avenues for further studies.

The explosive growth of the Internet of Things (IoT) is posing a high pressure on the spectrum resources. Dynamic spectrum allocation is an effective solution for supporting massive IoT devices with limited spectrum resources, often implemented in the form of spectrum auction. Among diverse auction formats, double auctions feature multiple buyers and multiple sellers, which is applicable to a wide range of IoT scenarios. Nevertheless, the existing double auction mechanisms mostly focus on the design of truthfulness and ignore the importance of privacy preservation. Hence, in this article, we propose PP-SPEC, a truthful and privacy-preserving double auction mechanism that takes spatial reuse, spectrum heterogeneity, and multiminded bidders into account, and provides full privacy in terms of asking prices, bidding values, and locations. To tackle the challenge of privacy-preserving comparison over ciphertext field, we carefully designed a set of generic privacy-preserving ciphertext comparison protocols (PIC), including basic PIC (B-PIC) and extended PIC (E-PIC), by integrating additive homomorphic encryption and garbled circuits. Furthermore, we have theoretically proved the truthfulness and security of PP-SPEC. We have implemented and evaluated PP-SPEC, whose experimental results show high performance and practicability of PP-SPEC for real-life applications.

Nowadays people join multiple social networks simultaneously to enjoy a variety of social services. Apart from common users, social networks also share other information entities such as organizations/companies. Aligning common information entities across heterogeneous social networks is challenging, but will facilitate many applications. Existing approaches for network alignment do not fully leverage indirect relations among users (i.e., friends of friends). This leads to relatively poor performance, especially when there are little overlapping social relations between different networks. Meanwhile, the mutual promotion between two kinds of information entity alignment is often neglected. In this paper, we propose a novel Matrix Factorization based Representation learning ( MFRep ) framework. MFRep investigates the joint learning for user and employer alignment across different networks. (1). We first compute the cross-network similarities in user attributes to extract seed potential anchor users. Its efficiency can be boosted by considering the similarities in employer properties. (2). Considering social relation differences across networks, we construct user relational matrix to preserve multi-step relational information. This embodies the information propagation from known and seed potential anchor users to other potential anchor users. (3). We extend the user relational matrix to preserve consistent associations between users and employers across networks. (4). We perform semi-supervised user representation learning and unsupervised employer representation learning concurrently via efficient matrix decomposition. The correspondence between users/employers across networks can be inferred based on vector relevance. Extensive experiments on real-world social network datasets justify the utility of MFRep , even with dissimilar social structures across networks. MFRep illustrates the mutual promoted alignments of users and employers with a middle degree of scalability.

The multi-UAV network is promising to extend conventional networks by providing broader coverage and better reliability. Nevertheless, the broadcast nature of wireless signals and the broader coverage expose multi-UAV communications to the threats of passive eavesdroppers. Recent studies mainly focus on securing a single legitimate link, or communications between a UAV and multiple ground users in one/two-UAV-aided networks, while the physical layer secrecy analysis for hierarchical multi-UAV networks is underexplored. In this paper, we investigate a general two-tier UAV network consisting of multiple UAV transmitters (UTs) and multiple UAV receivers (URs) in the presence of multiple UAV eavesdroppers (UEs). To protect all legitimate UT-UR links against UEs at the physical layer, we design a two-stage framework consisting of a UT-UR association stage and a cooperative transmission stage. Specifically, we formulate the secure transmission problem into a many-to-one matching game followed by an overlapping coalition formation (OCF) game, taking into account the limited capabilities and the throughput requirements of URs, as well as the transmission power constraints of UTs. A matching algorithm and an OCF algorithm are proposed to solve these two sequential games whose convergences and stabilities are guaranteed theoretically. Simulation results show the superiority of our algorithms and the effectiveness of our two-stage game framework in the terms of secrecy performance., Comment: to be appear in IEEE Transactions on Vehicular Technology

Deep neural networks have achieved tremendous success in various fields, especially in recognition and classification applications. However, faced with the difficulty of training millions of parameters of such networks, many users outsource the training procedure of a specific prediction work to the powerful cloud servers that own abundant computation and storage resources. Although such outsourced training can significantly simplify and expedite the development circles, it also introduces many security risks. In recent years, a new type of attack, the so-called backdoor attack, has attracted much attention, where the attacker's goal is to create a maliciously deep neural network to make misclassification on the special inputs with the backdoor trigger. For its concealment, such attacks can potentially cause disastrous consequences. Subsequently, many defense mechanisms against this attack are also appearing. In this article, we conduct a retrospective review on the existing schemes of the backdoor attacks and defenses in outsourced cloud environments. According to the resources the adversary has, and whether the detection time is during run-time or not, we classify the attack and defense approaches into multiple categories. We present a detailed overview of each category, and we provide a comparison of these approaches and evaluate part of the attack schemes by the experiments. We also highlight various future research directions in this field. These views shed light on possible avenues for future research.

Recent years have witnessed an exponential increase in the use of mobile and embedded devices. With the great success of deep learning in many fields, there is an emerging trend to deploy deep learning on mobile and embedded devices to better meet the requirement of real-time applications and user privacy protection. However, the limited resources of mobile and embedded devices make it challenging to fulfill the intensive computation and storage demand of deep learning models. In this survey, we conduct a comprehensive review on the related issues for deep learning on mobile and embedded devices. We start with a brief introduction of deep learning and discuss major challenges of implementing deep learning models on mobile and embedded devices. We then conduct an in-depth survey on important compression and acceleration techniques that help adapt deep learning models to mobile and embedded devices, which we specifically classify as pruning, quantization, model distillation, network design strategies, and low-rank factorization. We elaborate on the hardware-based solutions, including mobile GPU, FPGA, and ASIC, and describe software frameworks for mobile deep learning models, especially the development of frameworks based on OpenCL and RenderScript. After that, we present the application of mobile deep learning in a variety of areas, such as navigation, health, speech recognition, and information security. Finally, we discuss some future directions for deep learning on mobile and embedded devices to inspire further research in this area.

The dramatic growth of mobile application usage has posed great pressure on application developers to better manage their backend capacity. Rule-based or schedule-based auto-scaling mechanisms have been proposed, but it is difficult or expensive to frequently adjust the backend capacity to track the burstiness of mobile traffic. In this paper, we explore a fundamentally different approach. Instead of scaling the backend in line with the mobile traffic, we smooth out traffic profiles to reduce the required backend capacity and increase its utilization. Our proposed solution, called Razor , is inspired by two key insights on mobile traffic. First, mobile traffic exhibits high short-term fluctuations but steady long-term trend, so that we may temporarily delay user requests and periodically adapt backend capacity based on the predicted traffic volume. Second, user requests have different priorities: while some requests are urgent (e.g., sending a message), some are delay-tolerant (e.g., changing the profile photo) and can be postponed without much influence on the user experience. Based on these observations, our design features a two-tier architecture: on a long timescale, Razor predicts future traffic using machine learning algorithms and plans the optimal backend capacity to minimize the budget with performance guarantee; on a short timescale, Razor schedules which requests to delay and by how much time to delay according to their delay tolerance. We implement a fully-functional prototype of Razor, and evaluate its performance with both real and synthetic traces. Extensive experimental results show that Razor can effectively help mobile application developers reduce their backend cost while guaranteeing the user experience.

With the explosion of the Internet-of-Things (IoT) technology, numerous IoT terminal devices generate tremendous traffic. Device-to-device (D2D)-enabled caching can greatly relieve the pressure of massive resource-limited terminal devices in the IoT network. This article proposes a novel distributed framework, termed crowdcaching , which motivates selective file caching and cooperative file sharing among terminal devices via short-range (e.g., D2D) communications. After modeling file preference distributions and local connectivities, we optimize the caching strategy for any given coalition of cooperative users to minimize their total delay cost. In particular, if users have homogeneous file preferences and local connectivities, we can mathematically define a popularity index, according to which files are chosen to be cached in user devices. In a more general setting where users have heterogeneous file preferences and local connectivities, we propose a greedy algorithm of low complexity to determine the optimal caching strategy. Based on the cooperative caching strategy for any given coalition, we further investigate users’ incentive to form crowdcaching coalitions through the coalitional game theory and propose a distributed algorithm to yield a stable coalition formulation. The simulation results show that crowdcaching can effectively reduce the average delay cost of users by as much as 45.64%.

The rapid development of machine learning has spurred wide applications to various industries, where prediction models are built to forecast sales to help enterprises and governments make better plans. Alibaba Cloud and the Yancheng Municipal Government held a competition in 2018, calling for global efforts to build machine learning models that can accurately forecast vehicle sales based on large-scale datasets. This paper presents the design, implementation and evaluation of ForeXGBoost, and our proposed model that won the first place in the competition. ForeXGBoost takes full advantage of carefully-designed data filling algorithms for missing values to improve data quality. By using the sliding window to extract historical sales and production data features, ForeXGBoost can improve prediction accuracy. An extensive study is conducted to evaluate the influence of different attributes on vehicle sales via information gain and data correlation, based on which we select the most indicative features from the feature set for prediction. Furthermore, we leverage the XGBoost prediction algorithm to achieve a high prediction accuracy with short running time for vehicle sales prediction. Extensive experiments confirm that ForeXGBoost can achieve a high prediction accuracy with a low overhead.

The potential of multiuser MIMO (MU-MIMO) to increase network capacity has been intensively studied. To enable concurrent transmission in Frequency-Division Duplex (FDD) systems with limited feedback, users have to report the estimated channel state information (CSI) to the base station (BS) for interference elimination, which however has been proven to be vulnerable. In this paper, we reveal how to utilize the CSI feedbacks to launch sniffing attacks in a deterministic way, where the sniffing attack enables the attackers to eavesdrop other users in the same transmission group. We conduct a rigorous theoretical analysis on the construction of the forged CSI. In the proposed sniffing attacks, the malicious users can successfully sniff other users’ messages without knowing their own messages for signal cancellation. To make sniffing attacks more practical, we explore the possibilities of sniffing multiple users by a coalition of malicious users or a single malicious user. To thwart sniffing attacks, we design a novel secure CSI feedback (SCF) protocol based on the random masking technique, which requires simple modifications at the BS and incurs a little additional workload. Extensive theoretical analysis and experimental results are provided to further validate the effectiveness of our proposed sniffing attacks and the corresponding countermeasure.

Most existing systems use portable devices or image processing techniques for handwritten Chinese character recognition (HCCR), which are unable to detect character when writing on a paper or sensitive to lighting conditions. In this article, we present the design, implementation, and evaluation of a smartwatch-based HCCR system, called SmartHandwriting. To segment each Chinese character, we further analyze the hand movement between the handwriting gesture and the wrist movement gesture and propose a novel algorithm to distinguish the two types of gestures. Due to too many Chinese characters for classification, we utilize the data augmentation method for avoiding overfitting. Then, we build the HCCR model using the deep convolutional neural network (DCNN) method. The recognition accuracy of the Chinese characters is 96.0%, and extensive experiments confirm its effectiveness and robustness. Moreover, we also explore adverse factors that affect the recognition performance, which can be avoided in the future.

Voice authentication is becoming increasingly popular, which offers potential benefits over knowledge and possession based authentication methods. Meanwhile, the unique features of lip movements during speaking have been proved to be useful for authentication. However, the unimodal biometric authentication systems based on either voice or lip movements have certain limitations. Voice authentication systems are prone to spoofing attacks and suffer from serious performance degradation in noisy environments. Lip movements authentication systems are unstable and are sensitive to the user’s physical and psychological conditions. In this paper, we propose and implement LVID, a multimodal biometrics authentication system on smartphones, which resolves the defects of the original systems by combining the advantages of lip movements and voice. LVID simultaneously captures these two biometrics with the built-in audio devices on smartphones and fuses them at the data level. The reliable and effective features are then extracted from the fused data for authentication. LVID is practical as it requires neither cumbersome operations nor additional hardwares but only a speaker and a microphone that are commonly available on smartphones. Our experimental results with 104 participants show that LVID can achieve 95% accuracy for user authentication, and 93.47% of the attacks can be detected. It is also verified that LVID works well with different smartphones and is robust to different smartphone positions.

Performing the correct pen-holding gesture plays an important role in handwriting efficiency and quality, especially for early education. In this paper, a detailed design and evaluation of the system, called SmartGe, is presented, which can identify the pen-holding gesture with smartwatch when writing Chinese and English. We firstly analyze the hand movement and propose a novel handwriting detection algorithm to segment each stroke or letter. Then we recognize the pen-holding gesture using deep convolution neural network(DCNN). To improve system performance in Chinese writing, we connect a vertical stroke and a horizontal stroke for pen-holding gesture recognition. SmartGe provides a convenient and natural way to improve users' writing habits, which is a lightweight system, and extensive experiments confirm its effectiveness and robustness.

In the area of Internet of Things (IoT) voice assistants have become an important interface to operate smart speakers, smartphones, and even automobiles. To save power and protect user privacy, voice assistants send commands to the cloud only if a small set of pre-registered wake-up words are detected. However, voice assistants are shown to be vulnerable to the FakeWake phenomena, whereby they are inadvertently triggered by innocent-sounding fuzzy words. In this paper, we present a systematic investigation of the FakeWake phenomena from three aspects. To start with, we design the first fuzzy word generator to automatically and efficiently produce fuzzy words instead of searching through a swarm of audio materials. We manage to generate 965 fuzzy words covering 8 most popular English and Chinese smart speakers. To explain the causes underlying the FakeWake phenomena, we construct an interpretable tree-based decision model, which reveals phonetic features that contribute to false acceptance of fuzzy words by wake-up word detectors. Finally, we propose remedies to mitigate the effect of FakeWake. The results show that the strengthened models are not only resilient to fuzzy words but also achieve better overall performance on original training datasets.

More and more security situation awareness strategies in power network use deep learning as the data processing method. However, the endogenous security problem of deep learning poses a challenge to the robustness required for power network security situation awareness, and gives attackers an opportunity to add small perturbation to the network and fail the security situation awareness strategy. In this paper, we build the deep neural networks for the power network dataset. We verify a variety of adversarial example attack methods against deep neural networks, and tested the success rate of attacks on different types of networks to compare the difference of attack methods and target model. We can see from the experiment results that adversarial example attacks can have a great impact on the power network security situation awareness and pose a threat to the power network.

Cloud service providers, including Google, Amazon, and Alibaba, have now launched machine-learning-as-a-service (MLaaS) platforms, allowing clients to access sophisticated cloud-based machine learning models via APIs. Unfortunately, however, the commercial value of these models makes them alluring targets for theft, and their strategic position as part of the IT infrastructure of many companies makes them an enticing springboard for conducting further adversarial attacks. In this paper, we put forth a novel and effective attack strategy, dubbed InverseNet, that steals the functionality of black-box cloud-based models with only a small number of queries. The crux of the innovation is that, unlike existing model extraction attacks that rely on public datasets or adversarial samples, InverseNet constructs inversed training samples to increase the similarity between the extracted substitute model and the victim model. Further, only a small number of data samples with high confidence scores (rather than an entire dataset) are used to reconstruct the inversed dataset, which substantially reduces the attack cost. Extensive experiments conducted on three simulated victim models and Alibaba Cloud's commercially-available API demonstrate that InverseNet yields a model with significantly greater functional similarity to the victim model than the current state-of-the-art attacks at a substantially lower query budget.

The advent of new network architectures has resulted in the rise of network applications with different network performance requirements: live video streaming applications require low latency. In contrast, file transfer applications require high throughput. Existing congestion control protocols may fail to simultaneously meet the performance requirements of these different types of applications since their designed objective function is fixed and difficult to readjust according to the needs of the application. In this paper, we develop MOCC (Multi-Objective Congestion Control), a novel multi-objective congestion control protocol that can meet the performance requirements of different applications without the need to redesign the objective function. MOCC leverages multi-objective reinforcement learning with preferences in order to adapt to different types of applications. By addressing challenges such as slow convergence speed and the difficulty of designing the end of the episode, MOCC can quickly converge to the equilibrium point and adapt multi-objective reinforcement learning to congestion control. Through an extensive array of experiments, we discover that MOCC outperforms the most recent state-of-the-art congestion control protocols and can achieve a trade-off between throughput, latency, and packet loss, meeting the performance requirements of different types of applications by setting preferences.

Dynamic spectrum allocation via auction is an effective solution to spectrum shortage. Combinatorial spectrum auction enables buyers to express diversified preferences towards different combinations of channels. Despite the effort to ensure truthfulness and maximize social welfare, spectrum auction also faces potential security risks. The leakage of sensitive information such as true valuation and location of bidders may incur severe economic damage. However, there is a lack of works that can provide sufficient protection against such security risks in combinatorial spectrum auction. In this paper, we propose ARMOR, to enable combinatorial auction for heterogeneous spectrum with privacy, which can preserve bidders’ privacy while guaranteeing the economic-robustness of the combinatorial auction. We leverage the cryptographic methods, including homomorphic encryption, order-preserving encryption, and garbled circuits, to shield the bid and location information of buyers from the auctioneer. We design a novel location protection algorithm, which allows the auctioneer to exploit spectrum reuse opportunities without knowing the exact locations of buyers. Furthermore, we propose a verifiable payment scheme based on digital signature to prevent the auctioneer from forging the payment. The extensive experiments confirm that ARMOR maintains the good performance of the combinatorial spectrum auction, in terms of buyer satisfactory ratio and social welfare, and achieves privacy preservation with acceptable computation and communication costs.

Over the past decades, there have been extensive research endeavors in spectrum auction design. However, most solutions only focus on the allocation efficiency while ignoring the privacy leakage inherent in the process of spectrum auction. So far, the very few existing works on secure spectrum auctions either provide inadequate privacy protection or incur performance loss in terms of spectrum reusability. In this paper, for the first time, we propose PS-TAHES, a privacy-preserving and truthful double auction mechanism for heterogeneous spectrum. PS-TAHES is constructed based on our carefully designed security primitives, which can support various arithmetics over encrypted data, including multiplication, bid comparison, and sorting matrix, and they are well applicable in other contexts. We theoretically analyze the security and efficiency of PS-TAHES, which is proved to ensure a full and strong privacy protection for bidders while preserving the allocation efficiency of the original auction mechanism. Experimental results, consistent with the theoretical analysis, further validate the practical use of PS-TAHES in real-world applications.

Dual-channel screen-camera communication has been proposed to enable simultaneous screen viewing and hidden screen-camera communication. However, it strictly requires a well-controlled camera-screen alignment and an obstacle-free access. In this paper, we propose Dolphin, a novel real-time acoustics-based dual-channel communication system. Leveraging masking effects of human auditory system and readily available audio signals, Dolphin enables real-time unobtrusive speaker-microphone data communication without affecting the primary audio-hearing experience of human users. Compared with screen-camera communication, Dolphin supports non-line-of-sight transmissions and more flexible speaker-microphone alignments. Dolphin can also automatically adapt the data rate to various channel conditions. We further develop a secure data broadcasting scheme on Dolphin, where only designated privileged users can recover the embedded information in the acoustic signals. Our Dolphin prototype, built using COTS (Commercial Off-The-Shelf) smartphones, realizes (potentially secure) real-time hidden information communication, supports up to 8-meter signal capture distance and $\pm 90^{\circ }$ listening angle, and achieves an average goodput of 240 bps at 2 m.

Spectrum auction is an effective way to redistribute scarce spectrum resources. However, most spectrum auction designs only target at economic robustness, while neglecting the inherent privacy leakage problem. Existing secure spectrum auction mechanisms fail to provide adequate security, and they all neglect the online fashion of spectrum request arrival. In this paper, for the first time, we propose a Privacy-pReserving and truthful Online double auction mechanism for Spectrum allocaTion in wireless networks, PROST . Compared with the state-of-the-art solutions, PROST provides a comprehensive and strong protection for users’ sensitive information, especially for location privacy and time dynamics. PROST is constructed based on our carefully designed security building blocks, which support various arithmetics over encrypted real numbers, and they are also well applicable in other spectrum auctions. Besides, we improve on the existing online spectrum auction mechanisms by designing a novel privacy-preserving buyer grouping protocol for spectrum reuse. We not only theoretically prove that PROST can realize an all-round security against semi-honest adversaries but also extensively evaluate its performance. Experimental results validate that PROST achieves nice spectrum allocation efficiency with light computation and communication costs.

Edge computing plays an increasingly important role in the development of the Internet of Things (IoT). However, the explosive growth of terminal devices and unpredictable data flows make data transmission of edge computing systems challenging. In particular, it is crucial to efficiently transfer data from terminal devices to the edge server. In this paper, to ensure the fairness of network resource utilization and avoid starvation of low priority flows, we present a Collaborative Transmission Optimization Mechanism (CTOM) with priority and fairness. Based on the channel quality, CTOM first differentiates devices that can transmit data directly to the edge server from those that need to communicate with the edge server through relay nodes. To maximize transmission success ratio before deadlines of data flows, we perform flow scheduling based on its priority and allocate different bandwidth to different data flows to ensure fairness. In addition, we introduce the concept of energy harvesting (EH) for IoT devices. Equipped with an EH accessory, EH-enabled IoT devices extract energy from ambient resources, such as solar or radio frequency (RF) signals and thus improve energy efficiency. The simulation results verify that the transmission success ratio, the throughput, the average delay, and the energy consumption of CTOM are significantly improved compared with existing algorithms in the literature.

In this paper, we propose a novel ant colony optimization algorithm based on improved brainstorm optimization (IBSO-ACO) to solve the vehicle routing problem with soft time windows. Compared with the traditional ant colony algorithm, the proposed IBSO-ACO can better address the local optimum problem, since we have carefully designed an improved brainstorming optimization algorithm to update the solutions obtained by the ant colony algorithm, which enhance the solution diversity and the global search ability. Furthermore, we use the classification method to accelerate the convergence of the proposed algorithm. The extensive experimental results have confirmed that the proposed IBSO-ACO algorithm can achieve a lower routing cost at a high convergence rate than the traditional ant colony algorithm and the simulated annealing ant colony algorithm.

Nowadays, machine learning is becoming a new paradigm for mining hidden knowledge in big data. The collection and manipulation of big data not only create considerable values, but also raise serious privacy concerns. To protect the huge amount of potentially sensitive data, a straightforward approach is to encrypt data with specialized cryptographic tools. However, it is challenging to utilize or operate on encrypted data, especially to perform machine learning algorithms. In this paper, we investigate the problem of training high quality word vectors over large-scale encrypted data (from distributed data owners) with the privacy-preserving collaborative neural network learning algorithms. We leverage and also design a suite of arithmetic primitives (e.g., multiplication, fixed-point representation, sigmoid function computation, etc.) on encrypted data, served as components of our construction. We theoretically analyze the security and efficiency of our proposed construction, and conduct extensive experiments on representative real-world datasets to verify its practicality and effectiveness.

Reputation/trust-based blockchain systems have attracted considerable research interests for better integrating Internet of Things with blockchain in terms of throughput, scalability, energy efficiency, and incentive aspects. However, most existing works only consider static adversaries. Hence, they are vulnerable to slowly adaptive attackers, who can target validators with high reputation value to severely degrade the system performance. Therefore, we introduce zkRep, a privacy-preserving scheme tailored for reputation-based blockchains. Our basic idea is to hide both the identity and reputation of the validators by periodically changing the identity and reputation commitments (i.e., aliases), which makes it much more difficult for slowly adaptive attackers to identify validators with high reputation value. To realize this idea, we utilize privacy-preserving Pedersen-commitment-based reputation updating and leader election schemes that operate on concealed reputations within an epoch. We also introduce a privacy-preserving identity update protocol that changes the identity and time-window-based cumulative reputation commitments during each epoch transition. We have implemented and evaluated zkRep on the Amazon Web Service. The experimental results and analysis show that zkRep achieves great privacy-preserving features against slowly adaptive attacks with little overhead. Info-communications Media Development Authority (IMDA) National Research Foundation (NRF) Submitted/Accepted version This work was supported in part by RGC under Contract CERG 16204418, Contract 16203719, Contract 16204820, and Contract R8015; in part by the Guangdong Natural Science Foundation under Grant 2017A030312008; and in part by the National Research Foundation, Singapore, under its Strategic Capability Research Centres Funding Initiative

In recent years, artificial intelligence has been widely used in the field of network security, which has significantly improved the effect of network security analysis and detection. However, because the power industrial control system is faced with the problem of shortage of attack data, the direct deployment of the network intrusion detection system based on artificial intelligence is faced with the problems of lack of data, low precision, and high false alarm rate. To solve this problem, we propose an anomaly traffic detection method based on cross-domain knowledge transferring. By using the TrAdaBoost algorithm, we achieve a lower error rate than using LSTM alone.

Traditional congestion control algorithms were designed with a hardwired heuristic mapping between packet- level events and predefined control actions in response to these events, and may fail to satisfy all the desirable performance goals as a result. In this paper, we seek to reconsider these fundamental goals in congestion control, and propose Eagle, a new congestion control algorithm to refine existing heuristics. Eagle takes advantage of expert knowledge from an existing algorithm, and uses deep reinforcement learning (DRL) to train a generalized model with the hope of learning from an expert. Learning by trial-and-error may not be as efficient as imitating a teacher; by the same token, DRL alone is not enough to guarantee good performance. In Eagle, we seek help from an expert congestion control algorithm, BBR, to help us train a long-short term memory (LSTM) neural network in the DRL agent, with the hope of making decisions that can be as good as or even better than the expert. With an extensive array of experiments, we discovered that Eagle is able to match and even outperform the performance of its teacher, and outperformed a large number of recent congestion control algorithms by a considerable margin.

With the development of network, more and more unkown protocols appear. Network protocols define the rules between network entities and firewall uses network protocol for deep packet detection to prevent intrusions. For detecting these unkown protocols, firewall can’t analyze these protocols, which makes many systems vulnerable. To solve this problem, protocol reverse engineering is getting more and more attention. Protocol reverse engineering is a process that reverses the syntax and grammar of a protocol from its traces of execution codes. It focuses on three protocol features: field boundaries, protocol grammar and state machine. Field boundaries inference is the basis of the protocol reverse engineering, the precision of this process has a big influence on reversing the grammar and state machine. In this paper, we propose a method called ABinfer, which leverage the Field Adjacent information to identify the field boundaries. We evaluate the method on three protocols and the results show that it has a good ability to identify field boundaries of protocols.

To enable dynamic spectrum access, service providers with spare spectrum (sellers) trade with those who are in need of additional spectrum (buyers). In a spectrum market, the transaction result is essentially a matching between sellers and buyers. Though it is tempting to optimize the matching over certain utility functions, a stable matching is more desirable, since no participants have incentives to deviate from the matching result. Existing spectrum matching algorithms only consider the maximum number of channels a buyer can purchase, but ignore minimum spectrum requirement that is essential to support proper operation of wireless communications. In this paper, we present a new framework of spectrum matching with both maximum quota and minimum requirements. Different from conventional matching problems, the spectrum market poses distinctive challenges due to spectrum reusability. To tackle this problem, we design two novel algorithms that satisfy different stability criterion: Extended Deferred Acceptance (EDA) algorithm that is fair but wasteful and the Multistage Deferred Acceptance (MDA) algorithm that is non-wasteful but weakly fair. Both algorithms converge to an interference-free matching and guarantees the minimum spectrum requirement. The simulation results show that the two proposed algorithms can raise buyer happiness and the channel utilization.

Nowadays, smartphones have become indispensable for people and many users may browse information on their smartphones during walking. This raises severe concerns about pedestrian safety when distracted users inadvertently walks from the sidewalk onto the street. In this paper, we present WatchOut, a convenient pedestrian alerting system based on fine-grained step classification (e.g., flat, up and down the ramp) and event detection (e.g., entering the street, turn) in an urban environment. In contrast to existing systems that rely on shoe-mounted sensors, Watchout leverages rich sensors on the smartphone without requiring any additional hardwares. With carefully designed realtime data processing and classification algorithms, WatchOut can achieve a high detection accuracy. We develop a fully-functional Android application of WatchOut with user-friendly interface that will issue timely alerts to warn users of unsafe conditions. Extensive experiments with 23 volunteers in real pedestrian environment have confirmed the effectiveness of WatchOut, which achieves a comparable detection accuracy as the shoe-mounted system with off-the-shelf smartphones.

Ever-increasing demand for high-speed internet connections motivates cellular operators to upgrade their incumbent networks to the next-generation networks. In this correspondence paper, we study how an operator dynamically manages the budget flow during network upgrade to maximize the final-stage profit. Considering the time discount effect, we propose dynamic deployment policies for the next-generation network that trade off revenues and costs at each stage, and decide the optimal network expansion for the operator. We show that the time discount factor can have opposite effects to boost or hinder the network deployment, which depends on the depreciation of both the revenues and the costs. Furthermore, we extend our model and analysis to a market with more than one operator, where operators under radio access networks (RANs) sharing agreement jointly deploy the next-generation networks, and share the deployment and operational costs. A well-coordinated network upgrade under RAN sharing will speed up the deployment process and lead to a wider next-generation network coverage.

We present the design, implementation and evaluation of a smartwatch-based, freehand human–computer interaction system, called Magic-Hand, which consists of two modules: the operation detection module and the motion tracking module. In the operation detection module, five gestures are selected to represent typical operations including right-click, left-click and double-click, and we carefully design the detection algorithm to identify gestures even when the hand is in motion. In the motion tracking module, we map the movement of the hand to the screen cursor movement with the Sigmoid function. We verify the performance of Magic-Hand by comparing it with two existing human–computer interaction techniques: the mouse and the AirMouse. The experimental results show that the gesture recognition in the operation detection module can reach an accuracy of 95%, and the motion tracking is comparable with the benchmarks. Our work facilitates the use of the smartwatch as an effective interaction device in ubiquitous display environments.

With the growing popularity of cloud storage, to guarantee the security of outsourced data becomes more and more important. In this paper, we make the first attempt to explore the intrinsic relationship between secure cloud storage and homomorphic encryption scheme, based on which we present a Generic way to design a Secure Cloud Storage protocol, denoted as G-SCS , using any homomorphic encryption scheme (HES). The proposed G-SCS is secure under a definition that satisfy the security requirement of cloud storage. To address various issues in real application scenarios, we further extend the protocol to support deterministic and randomized auditing, data dynamics (i.e., data insertion, deletion and modification), as well as third-party public auditing, while preserving the efficiency and security of the protocol. By instantiating all abstract semantics in G-SCS , we construct three concrete secure cloud storage protocols using RSA-based, Paillier-based and DGHV-based HESs, which are multiplicatively, additively and fully HESs, respectively. We conduct extensive theoretical analysis and experimental evaluations to validate the practicability of the proposed protocol.

In the two-tier macro-femto heterogeneous network, hybrid access is regarded as the most ideal access control approach to mitigating macro-femto cross-tier interference and enhancing overall network performance. However, the implementation of hybrid access is hindered by a lack of incentive market mechanism to motivate private femtocell owners to offer access permissions to macro users. In this paper, we propose a reverse auction framework for access permission transaction between a macrocell operator and multiple femtocell owners to promote hybrid access. Our goal is to maximize social welfare while guaranteeing the truthfulness of the auction. Since the coverage of multiple femtocells may overlap, we partition each cell to adjust the granularity of access permission availability. We first propose a Vickery-Clarke-Grove (VCG)-based mechanism, which costs the least among all auction mechanisms that produce maximum social welfare. As the VCG mechanism is too time-consuming, we propose two alternative truthful mechanisms, namely, generalized second-price and suboptimal mechanism. We further extend the auction framework to the scenario where femtocell owners have heterogeneous valuations for access permissions in different locations.

The ever-increasing mobile video services and users’ demand for better video quality have boosted research into the video Quality-of-Experience. Recently, the concept of Quality-of-Experience has evolved to Quality-of-Engagement, a more actionable metric to evaluate users’ engagement to the video services and directly relate to the service providers’ revenue model. Existing works on user engagement mostly adopt uniform models to quantify the engagement level of all users, overlooking the essential distinction of individual users. In this paper, we first conduct a large-scale measurement study on a real-world data set to demonstrate the dramatic discrepancy in user engagement, which implies that a uniform model is not expressive enough to characterize the distinctive engagement pattern of each user. To address this problem, we propose PE, a personalized user engagement model for mobile videos, which, for the first time, addresses the user diversity in the engagement modeling. Evaluation results on a real-world data set show that our system significantly outperforms the uniform engagement models, with a 19.14% performance gain.

Performing the correct pen-holding gesture plays an important role in handwriting efficiency and quality, especially for early education. In this paper, we present the design, implementation and evaluation of SmartWriting, which utilizes the smartwatch for pen-holding gesture recognition for both Chinese and English writing. SmartWriting can automatically identify whether the user is writing Chinese or English, based on which two classifiers are built to detect pen-holding gestures by strokes in Chinese characters or English letters. In particular, we propose to leverage the combined signal of one vertical stroke and one horizontal stroke for efficient pen-holding gesture identification in Chinese writing, and we are able to infer pen-holding gesture according to any letter in English writing. We build a fully functional prototype of SmartWriting, and extensive experiments confirm its effectiveness and robustness. The detection accuracy is 9. % and 9 % for Chinese and English respectively. SmartWriting provides a natural, convenient and inexpensive way to improve users' writing habits.

Social tie prediction is an important issue in social network analysis. Transfer learning is often used for social tie prediction to address the problem of insufficient labeled training data, since few users manually annotate their social relationships. In this paper, we propose TranGAN, a novel generative adversarial network (GAN) based transfer learning framework for social tie prediction, which leverages social theories as the common knowledge to bridge the source network and the target network. GAN helps augment the original data set by generating data samples that have a similar probability distribution to that of the original data, and the training of TranGAN converges faster compared to existing transfer learning models. We evaluate the performance of TranGAN with extensive experiments, and show that TranGAN outperforms traditional learning algorithms and existing transfer learning algorithm on several metrics, and is efficient for large-scale social networks.

Voice biometrics is widely adopted for identity authentication in mobile devices. However, voice authentication is vulnerable to spoofing attacks, where an adversary may deceive the voice authentication system with pre-recorded or synthesized samples from the legitimate user or by impersonating the speaking style of the targeted user. In this paper, we design and implement VoicePop, a robust software-only anti-spoofing system on smartphones. VoicePop leverages the pop noise, which is produced by the user breathing while speaking close to the microphone. The pop noise is delicate and subject to user diversity, making it hard to record by replay attacks beyond a certain distance and to imitate precisely by impersonators. We design a novel pop noise detection scheme to pinpoint pop noises at the phonemic level, based on which we establish individually unique relationship between phonemes and pop noises to identify legitimate users and defend against spoofing attacks. Our experimental results with 18 participants and three types of smartphones show that VoicePop achieves over 93.5% detection accuracy at around 5.4% equal error rate. VoicePop requires no additional hardware but only the built-in microphones in virtually all smartphones, which can be readily integrated in existing voice authentication systems for mobile devices.

With powerful parallel computing GPUs and massive user data, neural-network-based deep learning can well exert its strong power in problem modeling and solving, and has archived great success in many applications such as image classification, speech recognition and machine translation etc. While deep learning has been increasingly popular, the problem of privacy leakage becomes more and more urgent. Given the fact that the training data may contain highly sensitive information, e.g., personal medical records, directly sharing them among the users (i.e., participants) or centrally storing them in one single location may pose a considerable threat to user privacy. In this paper, we present a practical privacy-preserving collaborative deep learning system that allows users to cooperatively build a collective deep learning model with data of all participants, without direct data sharing and central data storage. In our system, each participant trains a local model with their own data and only shares model parameters with the others. To further avoid potential privacy leakage from sharing model parameters, we use functional mechanism to perturb the objective function of the neural network in the training process to achieve $\epsilon $ -differential privacy. In particular, for the first time, we consider the existence of unreliable participants , i.e., the participants with low-quality data, and propose a solution to reduce the impact of these participants while protecting their privacy. We evaluate the performance of our system on two well-known real-world datasets for regression and classification tasks. The results demonstrate that the proposed system is robust against unreliable participants, and achieves high accuracy close to the model trained in a traditional centralized manner while ensuring rigorous privacy protection.

IPTV provides a variety of services to users, e.g., Live TV, Video on Demand (VOD), and Catch-up TV, which allows users to freely select from a huge pool of program genres. A high-quality program recommendation system that can well predict users' dynamic preferences is desirable to improve user satisfaction. In this paper, we make the first attempt to design a personalized TV program recommendation system based on tensor learning that leverages temporal context information and implicit feedback. We establish the user preference model by carefully analyzing the influence of program genres and time context on users' viewing behavior in IPTV viewing logs. To obtain the optimal program recommendation, we adopt tensor decomposition to mine the latent relationship among users, program genres and the time contexts, while address issues of data sparsity and missing information. Our extensive experimental results on a real dataset from a major IPTV service provider in China have confirmed the superiority of our proposed recommendation algorithm over baseline algorithms in achieving a higher recommendation accuracy and covering a wider range of programs.