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A key factor in big data analytics and artificial intelligence is the collection of user data from a large population. However, the collection of user data comes at the price of privacy risks, not only for users but also for businesses who are vulnerable to internal and external data breaches. To address privacy issues, local differential privacy (LDP) has been proposed to enable an untrusted collector to obtain accurate statistical estimation on sensitive user data (e.g., location, health, and financial data) without actually accessing the true records. As key-value data is an extremely popular NoSQL data model, there are a few works in the literature that study LDP-based statistical estimation on key-value data. However, these works have some major limitations, including supporting small key space only, fixed key collection range, difficulty in choosing an appropriate padding length, and high communication cost. In this paper, we propose a two-phase mechanism PrivKVM* as an optimized and highly-complete solution to LDP-based key-value data collection and statistics estimation. We verify its correctness and effectiveness through rigorous theoretical analysis and extensive experimental results.

Machine learning service API allows model owners to monetize proprietary models by offering prediction services to third-party users. However, existing literature shows that model parameters are vulnerable to extraction attacks which accumulate prediction queries and their responses to train a replica model. As countermeasures, researchers have proposed to reduce the rich API output, such as hiding the precise confidence. Nonetheless, even with response being only one bit, an adversary can still exploit fine-tuned queries with differential property to infer the decision boundary of the underlying model. In this paper, we propose boundary differential privacy (BDP) against such attacks by obfuscating the prediction responses with noises. BDP guarantees an adversary cannot learn the decision boundary of any two classes by a predefined precision no matter how many queries are issued to the prediction API. We first design a perturbation algorithm called boundary randomized response for a binary model. Then we prove it satisfies e-BDP, followed by a generalization of this algorithm to a multiclass model. Finally, we generalize a hard boundary to soft boundary and design an adaptive perturbation algorithm that can still work in the latter case. The effectiveness and high utility of our solution are verified by extensive experiments on both linear and non-linear models.

Due to the severe speckle noise and complex local deformation in synthetic aperture radar (SAR) images, robust pixelwise registration with high accuracy is an important problem but is far from being resolved. The core of this problem is how to establish a precise deformation field that maps every pixel to its corresponding pixel with high accuracy. To address this problem, a novel SAR dense-matching algorithm, which includes high-accuracy landmark generation and a precise deformation field parameter estimation, is proposed in this article. First, a strategy for generating enough well-distributed landmarks is proposed by designing patch matching of improved scale-invariant feature transform features based on phase correlation and the gradient method. Furthermore, a multiscale coherent point drift (MCPD), powered by iterative residual map minimization, is designed to reliably match landmarks and estimate precise field parameters. Both simulated deformed SAR images and real SAR images are utilized to evaluate the performance of the proposed method, and the experimental results demonstrate that the proposed method provides better registration performance than previous methods in terms of both accuracy and robustness.

Live video streaming has seen tremendous growth in the past decade. An important fact in live streaming is that the demand for low playback-latency inherently conflicts with the desire for high QoE. This requires different types of live services to seek different latency-QoE tradeoffs according to their service-requirements. However, our investigations revealed that it is fundamentally difficult for existing streaming algorithms to keep consistent latency in changing network conditions, let alone achieve the service-desired latency-QoE tradeoff. To tackle the challenge, this article develops a novel framework called Flexible Latency Aware Streaming (FLAS) that not only can achieve consistent low latency, but also control the latency-QoE tradeoff flexibly. Specifically, FLAS generates a set of adaptation logics offline, each optimized for a candidate tradeoff point, then selects the most appropriate one to run online. We first show how FLAS can be applied to optimizing the existing algorithms, then developed a novel Genetic Programming approach to fully exploit FLAS's potential. Extensive evaluations show that FLAS can precisely control latency all the way down to 1s and achieve substantially higher QoE than state-of-the-arts. FLAS can be readily implemented into real streaming platforms, offering a practical and reliable solution for live-streaming services.

Due to the increasing cost of data storage and computation, more and more graphs (e.g., web graphs, social networks) are outsourced and analyzed in the cloud. However, there is growing concern on the privacy of these outsourced graphs at the hands of untrusted cloud providers. Unfortunately, simple label anonymization cannot protect nodes from being re-identified by adversary who knows the graph structure. To address this issue, existing works adopt the k-automorphism model, which constructs $$(k-1)$$ symmetric vertices for each vertex. It has two disadvantages. First, it significantly enlarges the graphs, which makes graph mining tasks such as subgraph matching extremely inefficient and sometimes infeasible even in the cloud. Second, it cannot protect the privacy of attributes in each node. In this paper, we propose a new privacy model (k, t)-privacy that combines the k-automorphism model for graph structure with the t-closeness privacy model for node label generalization. Besides a stronger privacy guarantee, the paper also optimizes the matching efficiency by (1) an approximate label generalization algorithm TOGGLE with $$(1+\epsilon )$$ approximation ratio and (2) a new subgraph matching algorithm PGP on succinct k-automorphic graphs without decomposing the query graph.

The widely application of positioning technology has made collecting the movement of people feasible for knowledge-based decision. Data in its original form often contain sensitive attributes and publishing such data will leak individuals’ privacy. Especially, a privacy threat occurs when an attacker can link a record to a specific individual based on some known partial information. Therefore, maintaining privacy in the published data is a critical problem. To prevent record linkage, attribute linkage, and similarity attacks based on the background knowledge of trajectory data, we propose a data privacy preservation with enhanced l-diversity. First, we determine those critical spatial-temporal sequences which are more likely to cause privacy leakage. Then, we perturb these sequences by adding or deleting some spatial-temporal points while ensuring the published data satisfy our (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L,\alpha ,\beta $$\end{document}L,α,β)-privacy, an enhanced privacy model from l-diversity. Our experiments on both synthetic and real-life datasets suggest that our proposed scheme can achieve better privacy while still ensuring high utility, compared with existing privacy preservation schemes on trajectory.

The growing number of mobile and IoT devices has nourished many intelligent applications. In order to produce high-quality machine learning models, they constantly access and collect rich personal data such as photos, browsing history, and text messages. However, direct access to personal data has raised increasing public concerns about privacy risks and security breaches. To address these concerns, there are two emerging solutions to privacy-preserving machine learning, namely local differential privacy and federated machine learning. The former is a distributed data collection strategy where each client perturbs data locally before submitting to the server, whereas the latter is a distributed machine learning strategy to train models on mobile devices locally and merge their output (e.g., parameter updates of a model) through a control protocol. In this article, we conduct a comparative study on the efficiency and privacy of both solutions. Our results show that in a standard population and domain setting, both can achieve an optimal misclassification rate lower than 20% and federated machine learning generally performs better at the cost of higher client CPU usage. Nonetheless, local differential privacy can benefit more from a larger client population ($>$> 1k). As for privacy guarantee, local differential privacy also has flexible control over the data leakage.

Netflow log files commonly contain massive transfer records in tiny time interval, making analytical works complex and burdensome. By combining human cognition abilities with computerized techniques, visual analytics systems have become efficient tools for showing network states and locating abnormal behaviors. However, traditional visual analytics systems tend to be designed for solving certain problems and unable to synthesize various types of data sources. Despite recent advances in network security visualization, academia still starves for a proper solution to visualize IPv4 address behavior modes and IPv4 connection patterns within limited drawing space. Thus, we propose a visual analytics system called `Owleyes' which reprocesses Netflow log data with simple statistical operations in basic dimensions and fulfills the aforementioned requirements with proper novel graphs such as `sunburst-hive-plot graph' (SHG) and link-wheel graph (LW). The SHG provides a stable and comparable means of visualizing connection patterns efficiently in a limited drawing space. The LW represents the hourly connection counts of main ports in a specific IPv4 connection during one day. With the use case dealing with the ChinaVis 2016 Challenge I data, the efficiency and practicability of Owleyes are demonstrated.

Privacy concerns on smartphones have been raised by the public as more and more personal data are now stored on them. In this paper, we show that location information can be compromised through mobile inertial sensors which are considered insensitive and accessible by any mobile application in both iOS and Android without special privilege. We present MISSILE, an automatic system that can infer users’ indoor location using labeled sensor data as prior knowledge. The key idea is that when a user reaches a particular indoor location, it is very likely that he/she has passed through some unique interior structures of a building, such as winding corridors, fire stop doors or elevators. These structures exhibit repeatable motion and environment patterns in mobile sensors that can be recognized by supervised learning. In our MISSILE system, the location labels of training data are automatically attained by Bluetooth beacons deployed in sensitive locations. With effective feature extraction procedure robust modeling, MISSILE shows good success rate for inference attack. For example, in a university campus with 15 sensitive locations, MISSILE achieves up to 73% correct prediction score whereas a random guess can only achieve 1/(15 + 1) = 6.25%. Further improvements on system performance and countermeasures are also discussed.

Local differential privacy (LDP) is a promising privacy model for distributed data collection. It has been widely deployed in real-world systems (e.g. Chrome, iOS, macOS). In LDP-based mechanisms, an aggregator collects private values perturbed by each user and then analyses these values to estimate their statistics, such as frequency and mean. Most existing works focus on simple scalar value types, such as boolean and categorical values. However, with the emergence of smart sensors and internet of things, high-dimensional data are gaining increasing popularity. In many cases, correlations exist between various attributes of such data, e.g. temperature and luminance. To ensure LDP for high-dimensional data, existing solutions either partition the privacy budget ϵ among these correlated attributes or adopt sampling, both of which dilute the density of useful information and thus result in poor data utility.In this paper, we propose a relaxed LDP model, namely, univariate dominance local differential privacy (UDLDP), for high-dimensional data. We quantify the correlations between attributes and present a correlation-bounded perturbation (CBP) mechanism that optimizes the partitioning of privacy budget on each correlated attribute. Furthermore, we extend CBP to support sampling, which is a common bandwidth reduction technique in sensor networks and Internet of Things. We derive the best allocation strategy of sampling probabilities among attributes in terms of data utility, which leads to the correlation-bounded perturbation mechanism with sampling (CBPS). The performance of both mechanisms is evaluated and compared with state-of-the-art LDP mechanisms on real-world and synthetic datasets.

Time series has numerous application scenarios. However, since many time series data are personal data, releasing them directly could cause privacy infringement. All existing techniques to publish privacy-preserving time series perturb the values while retaining the original temporal order. However, in many value-critical scenarios such as health and financial time series, the values must not be perturbed whereas the temporal order can be perturbed to protect privacy. As such, we propose "local differential privacy in the temporal setting" (TLDP) as the privacy notion for time series data. After quantifying the utility of a temporal perturbation mechanism in terms of the costs of a missing, repeated, empty, or delayed value, we propose three mechanisms for TLDP. Through both analytical and empirical studies, we show the last one, Threshold mechanism, is the most effective under most privacy budget settings, whereas the other two baseline mechanisms fill a niche by supporting very small or large privacy budgets.

Previous approaches to background subtraction in freely moving camera typically focus on improving the accuracy of motion estimation. In this paper, we propose that the accurate background subtraction is possible with the integration of alternative cues about foreground and background. We also put forward a novel background subtraction framework called the integration of foreground and background cues. Here, the foreground cues are extracted by the Gaussian mixture model compensated with image alignment, while the background cues are obtained from the spatiotemporal features filtered by the homography transformation. Subsequently, the integration is devised as a hierarchical competition procedure based on super-pixels under multiple levels with the underlying motivation to utilize the exclusiveness between these cues for the compensation of their corresponding defects. The result of competition between foreground and background cues in a particular super-pixel is used as the proximity, and the foreground is segmented by combining super-pixels with proximity under multiple levels. Comprehensive evaluations using standard benchmarks demonstrate the superiority of our work compared with the state-of-the-art.

Planning and managing requirements changes in Global Software Development (GSD) is a challenging task. While requirements change has received much attention from researchers, Requirements Change Management (RCM) process is still an emerging area in GSD. The objective of this paper is to identify the success factors of RCM in the GSD environment. We applied the Systematic Literature Review (SLR) approach and identified 23 success factors that influence RCM in GSD projects. The findings of the SLR indicate that change impact analysis, change understanding, management support, RCM process awareness, standard for RCM, progress measure, updated requirements and minimize project failure risk are key factors that influence RCM in a GSD project. We present a comparison of success factors identified in client and vendor organizations. Moreover, we present a framework to classify the identified success factors for RCM process implementation. We believe GSD organizations can use the framework to better manage requirements change in GSD projects.

Crowd counting is a challenging task due to the influence of various factors, such as scene transformation, complex crowd distribution, uneven illumination, and occlusion. To overcome such problems, scale-adaptive convolutional neural network (SaCNN) used a convolutional neural network to obtain high-quality crowd density map estimation and integrate the density map to get the estimated headcount. To obtain better performance on crowd counting, an improved crowd counting method based on SaCNN was proposed in this paper. The spread parameter, i.e., the standard variance, of geometry-adaptive Gaussian kernel used in SaCNN was optimized to generate a higher quality ground truth density map for training. The absolute count loss with weight 4e-5 was used to jointly optimize with the density map loss to improve the network generalization ability for crowd scenes with few pedestrians. Also, a random cropping method was applied to improve the diversity of training samples to enhance network generalization ability. The experimental results upon ShanghaiTech public dataset showed that the proposed method can obtain more accurate and more robust results on crowd counting than those of SaCNN.

Deep learning algorithms have been widely applied to the recognition of remote-sensing images due to their excellent performance on various recognition problems with sufficient data. However, limited data on synthetic aperture radar (SAR) images degrade the performance of neural networks for SAR automatic target recognition (ATR). To address this problem, this paper presents a new deep feature fusion framework by combining the Gabor features and information of raw SAR images and fusing the feature vectors extracted from different layers in our proposed neural network. Gabor features improve the richness of SAR image features. The number of free parameters of neural networks is largely reduced by utilizing large-scale convolutional kernel factorization and global average pooling. Moreover, the fusion of feature vectors from different layers helps improve the recognition performance of neural networks. Experimental results on the MSTAR dataset demonstrate the effectiveness of our proposed method. The proposed neural network can achieve an average accuracy of 99% on the classification of ten-class targets and even achieve a high recognition accuracy on limited data and noisy data.

For two-dimensional (2D) direction of arrival (DOA) estimation in a uniform rectangular array (URA), the conventional method converts the 2D problem into a one-dimensional (1D) problem; however, the computational complexity is high, and the accuracy is limited by the grid interval. To address this issue, based on sparse representation theory and a separable observation model (SPM), this paper presents a novel off-grid-framework-based 2D DOA estimation approach by designing a modified 2D off-grid model and a solution for the multisnapshot case in the SPM. The proposed algorithm can be divided into two stages. In the first stage, we use a matching pursuit and focal underdetermined system solver (MFOCUSS) algorithm to quickly identify the candidate or potential areas where the true sources may exist. In the second stage, the candidate areas obtained in the first stage are regarded as the initialization. Then, for a specific source, we regard other sources as interference. By using an alternating descent method, we can obtain accurate DOAs. Moreover, based on the equivalence of time delay and spatial spacing, a 2D off-grid method for multisnapshot cases is proposed in this paper. Numerical simulations demonstrate the effectiveness and efficiency of the proposed algorithm.

In recent years, social networks have attracted the interest of researchers from diverse disciplines. Competing types of information, such as positive and negative information about a topic or marketing information for similar products from different companies, often diffuse in social networks simultaneously. However, most previous studies only consider one type of information using the susceptible-infectious-recovered or susceptible-infectious-susceptible models. In this paper, we propose a competitive diffusion model to describe the diffusion processes of two types of information in social networks, analyze the stability of the diffusion infection-free equilibrium, and study the diffusion processes by numerical simulations. The results show that the information with the larger probabilities of being received and reposted diffuses more broadly and suppresses the competitor. We also find that the initial number of spreader nodes, the degrees of participants, and network structures all affect the scope of information diffusion.

Context modeling is often used to relate the context in which a system will operate to the entities of interest in the problem domain. It remains the case that context models are inadequate in emerging computing paradigms (e.g., smart spaces and the Internet of Things), in which the relevance of context is shaped dynamically by the changing needs of users. Formal models are required to fuse and interpret contextual information obtained from the heterogeneous sources. In this paper, we propose an integrated and formal context modeling approach for intelligent systems operating in the context-sensitive environments. We introduce a goal-driven, entity-centered identification method for determining which context elements are influential in adapting the system behavior. We then describe a four-layered framework for metamodeling the identification and management of context. First, the framework presents a formal metamodel of context. A formalization of context using the first-order logic with relational operators is then presented to specify formally the context information at different abstraction levels. The metamodel, therefore, prepares the ground for building a formal modeling language and automated support tool (https://github.com/metamodeler/CIM-CSS/). The proposed model is then evaluated using an application scenario in the smart meeting rooms domain, and the results are analyzed qualitatively.

This paper proposes a black box extraction attack model on pre-trained image classifiers to rebuild a functionally equivalent model with high similarity. Common model extraction attacks use a large number of training samples to feed the target classifier which is time-consuming with redundancy. The attack results have a high dependency on the selected training samples and the target model. The extracted model may only get part of crucial features because of inappropriate sample selection. To eliminate these uncertainties, we proposed the VAE-kdtree attack model which eliminates the high dependency between selected training samples and the target model. It can not only save redundant computation, but also extract critical boundaries more accurately in image classification. This VAE-kdtree model has shown to achieve around 90% similarity on MNIST and around 80% similarity on MNIST-Fashion with a target Convolutional Network Model and a target Support Vector Machine Model. The performance of this VAE-kdtree model could be further improved by adopting higher dimension space of the kdtree.

1. This chapter thinks that the management of the relationship between man and nature mainly includes three parts “Complying with the Tao” (Management Based on Tao), “Following with the Rules” (Management Based on Rules), “Emphasizing the Methods” (Management Based on Methods). In the subordinate branches of Chinese management principles, these three parts are logically closely integrated and inseparable. The relationship between man and nature is the premise of all relations. Only by dealing with the relationship between them can we better handle the relationship between human and society and between human and human. 2. Shun Tao is based on the correct understanding of the world’s Tao to follow Tao’s requirements and achieve certain governance objectives of management behavior. Following the Rules is the management process in which the manager implements the management behavior, conforms to the development rules, and achieves a certain goal according to an effective way. Emphasizing on Methods is to use a variety of effective management methods, combined with the situation at the time to practice and explore, so that management behavior can fully play its role. 3. In the process of dealing with the relationship between human and nature, we should proceed from the integrity of things, use effective and expedient management methods, follow the development trend of the laws of nature, society and humanity, seize the opportunity and favorable situation, and constantly innovate, so as to make the management process more effective and reasonable. 4. In the practical management, people should constantly improve their cognitive ability and level, and improve their personal accomplishment; It is necessary to follow the development direction of the natural law, achieve the harmony between man and nature, and constantly create favorable opportunities and situations so that the internal mechanism of nature can be used in human management activities.

Password-based authentication is essential to any online service. It is normally powered by a database of user credentials, for example a RADIUS server. However, even with various indexing techniques (e.g., B+-tree), password-based authentication can still be resource-consuming on large-scale systems (e.g., Internet and IoT), and is thus vulnerable to distributed denial-of-service (DDoS) attacks.In this paper, we propose a cloud-based firewall that uses Bloom filters to pre-screen and reject suspicious requests with wrong password before they reach the authentication server. The main challenge is the security of the firewall because it can be operated by a third party, so the Bloom filters might be accessed by adversaries to assist their brute-force password guessing.To ensure security, we start with the assumption of trusted cloud server and design a key-based semantic secure Bloom filter (KSSBF) for the best efficiency. We then design a generically secure Bloom filter (GSBF) for non-trusted cloud servers, which is key-independent and with strictly provable security. Through theoretical and empirical analysis, we show both of them can mitigate malicious requests without compromising the security of passwords.

Data aggregation is a key problem in wireless sensor networks (WSNs). To secure the aggregation results, researchers have proposed to adopt homomorphic encryptions. Since aggregation is conducted in the ciphertext space without decryption, both the confidentiality and integrity can be protected against untrusted or compromised aggregators. However, such techniques cannot protect against untrusted or compromised sources, i.e., wireless sensors, as homomorphic encryptions require all sources to share a common encryption key. Since wireless sensor networks are often vulnerable to physical or network attacks, new secure aggregation schemes that can protect against compromised sources are needed. This paper proposes Onion Homomorphic Encryption-based Aggregation (OHEA), where sources form groups with their dedicated encryption keys, a.k.a., the group keys. OHEA has a nice property that group keys themselves can be aggregated, so it can work recursively with any level of aggregation hierarchy. By security analysis, we show that even if multiple aggregators or sources are compromised, an adversary is still unable to compromise the data of other nodes in the same or upper levels of the hierarchy. Furthermore, the experimental results show that OHEA incurs low computation and communication cost, and is thus scalable to large WSNs.

It is very crucial for an organization to encapsulate the requirements in its early stage when they are intending to build a novel system such as the internet of things (IoT), particularly when it comes to capturing privacy and security requirements to gain the public confidence. The proposed research is focused to develop a secure IoT-requirement implementation maturity model (SIOT-RIMM). The proposed model will assist the software development organizations to improve and modify their requirement engineering processes in terms of security and privacy of IoT. The SIOT-RIMM model will be developed based on the existing IoT literature pertaining to security and privacy, industrial empirical study and understanding of the challenges that could negatively influence the implementation of security and privacy in IoT. To develop the maturity levels of SIOT-RIMM, we will consider the concepts of existing maturity models of other software engineering domains. In this preliminary study, 19 challenges were identified using the SLR approach that might have a negative impact on the IoT requirements engineering process. The identified challenges will contribute to the development of SIOT-RIMM maturity levels.

Local differential privacy (LDP) is an emerging technique for privacy-preserving data collection without a trusted collector. Despite its strong privacy guarantee, LDP cannot be easily applied to real-world graph analysis tasks such as community detection and centrality analysis due to its high implementation complexity and low data utility. In this paper, we address these two issues by presenting LF-GDPR, the first LDP-enabled graph metric estimation framework for graph analysis. It collects two atomic graph metrics — the adjacency bit vector and node degree — from each node locally. LF-GDPR simplifies the job of implementing LDP-related steps (e.g., local perturbation, aggregation and calibration) for a graph metric estimation task by providing either a complete or a parameterized algorithm for each step.

Local Differential Privacy (LDP), where each user perturbs her data locally before sending to an untrusted party, is a new and promising privacy-preserving model. Endorsed by both academia and industry, LDP provides strong and rigorous privacy guarantee for data collection and analysis. As such, it has been recently deployed in many real products by several major software and Internet companies, including Google, Apple and Microsoft in their mainstream products such as Chrome, iOS, and Windows 10. Besides industry, it has also attracted a lot of research attention from academia. This tutorial first introduces the rationale of LDP model behind these deployed systems to collect and analyze usage data privately, then surveys the current research landscape in LDP, and finally identifies several open problems and research directions in this community.

The premise of dealing with uncertainty in this article is to figure out the impact of uncertainties on project performance. This article proposes a framework for quantitative analysis that combine...

The traditional route planning algorithm has some problems, such as the ambiguity of objective function and the poor convergence, etc. Therefore, a tourism route optimization planning algorithm bas...

Quality is the most important factor for software development as it mainly defines customer satisfaction that is directly related to the success of a software project. The software process model is used to ensure software quality, represent a variety of task settings, manage project duration, improve the process and range to execute the process understanding, and to appropriate implicit conjecture for all task settings. Several software processes models exist in software albeit with limited scope. Given this viewpoint, this paper presents a new software development life cycle model, “AZ-Model,” for software development by introducing new activities during software development life cycle. It overcomes the limitations of traditional models and significantly impacts the production of a quality product in a time-box. This paper also presents a comprehensive comparative study and statistical analyses to examine the significance of AZ–Model for software development.

Malicious websites are increasing in abundance, which brings serious web security threats to users. As a result, individuals lose their assets, values, information, etc. to unauthorized parties and become victims while visiting such websites. The research community put efforts into developing effective and efficient models for detecting malicious URLs to make available notifications about websites that users access. Perceiving this, numerous methods make use of various types of machine learning (ML) approaches. However, until now, no technique has perfectly detected malicious URLs, as they are susceptible to false positive and false negative decisions in classifying URLs in malicious and non-malicious groups. To improve this issue, this article proposes a new approach based on a machine learning technique. The proposed approach uses multiple classifiers (i.e., ensemble learning) to predict the class probabilities of URLs and then applies a threshold to filter the decisions of multiple classifiers. Next, the decisions are combined concerning its corresponding class probabilities and find the class label with the highest class probability as the final decision on unlabeled URLs. The results show that the proposed method offers better performance in terms of malicious URL detection than other methods.

Since the boom of smartphones and location-based services, spatio-temporal data (i.e., user locations with timestamps) have become increasingly essential in many real-life applications. To ensure these data are faithfully extracted from the underlying location tracking hardware and not altered by any malicious party or the user himself/herself, integrity assurance schemes such as digital signatures or message authentication codes (MAC) must be adopted. However, these conventional schemes disclose to the verifier the complete plaintext location and thus jeopardize users’ privacy. In this paper, we propose an integrity assurance scheme with minimum location disclosure. That is, the granule of the disclosed location is just small enough to prove the user is/has been to a certain place, and the verifier cannot learn anything beyond it. To this end, we propose a new MAC scheme called Prefix-verifiable MAC (PMAC), based on which we design indexes and protocols to authenticate both spatial and spatio-temporal predicates. Security analysis and experimental results show our scheme is both secure and efficient for practical use.

Probabilistic top- $k$ ranking is an important and well-studied query operator in uncertain databases. However, the quality of top- $k$ results might be heavily affected by the ambiguity and uncertainty of the underlying data. Uncertainty reduction techniques have been proposed to improve the quality of top- $k$ results by cleaning the original data. Unfortunately, most data cleaning models aim to probe the exact values of the objects individually and therefore do not work well for subjective data types, such as user ratings, which are inherently probabilistic. In this paper, we propose a novel pairwise crowdsourcing model to reduce the uncertainty of top- $k$ ranking using a crowd of domain experts. Given a crowdsourcing task of limited budget, we propose efficient algorithms to select the best object pairs for crowdsourcing that will bring in the highest quality improvement. Extensive experiments show that our proposed solutions outperform a random selection method by up to 30 times in terms of quality improvement of probabilistic top- $k$ ranking queries. In terms of efficiency, our proposed solutions can reduce the elapsed time of a brute-force algorithm from several days to one minute.

The prosperity of location-based social networking has paved the way for new applications of group-based activity planning and marketing. While such applications heavily rely on geo-social group queries (GSGQs), existing studies fail to produce a cohesive group in terms of user acquaintance. In this paper, we propose a new family of GSGQs with minimum acquaintance constraints. They are more appealing to users as they guarantee a worst-case acquaintance level in the result group. For efficient processing of GSGQs on large location-based social networks, we devise two social-aware spatial index structures, namely SaR-tree and SaR*-tree. The latter improves on the former by considering both spatial and social distances when clustering objects. Based on SaR-tree and SaR*-tree, novel algorithms are developed to process various GSGQs. Extensive experiments on real datasets Gowalla and Twitter show that our proposed methods substantially outperform the baseline algorithms under various system settings.

Wireless sensor networks (WSNs) have been used extensively in a range of applications, which realizes data acquisition, processing, transmission, and analysis in an interesting area. Harsh surroundings and their inherent vulnerability often mean that these networks suffer from simultaneous node failure possibly causing the network to become partitioned into multiple disjointed segments. This in turn can prevent the gathering of data from the sensors and subsequent transmission to the sink, causing the whole network to fail. In this paper, a strategy is presented for restoring multi-objective optimization connectivity of these segments using mobile data collectors (MDCs), by considering the segments as collections of sensor nodes and not as some representative node. Different from existing uses of MDCs for restoration, the delay in data collection and task balance is considered, and the network connectivity and data acquisition path optimization problem are transformed into an improved multi-travelling salesman problem (iMTSP). An improved multi-objective optimization genetic algorithm for solving the optimal collection data collector position and moving paths is proposed, which introduces virtual segments and hierarchical chromosome structure, improved population diversity, and custom coding and decoding. The simulation results show that the proposed method can effectively solve the iMTSP of the Pareto optimal solution and can provide a new strategy for connectivity-restoring technology in WSNs. Compared with NSGA-II, the diversity of the proposed gene algorithm represents a clear improvement.

Checking the design specification for contradictions at the early phase of the software development process is crucial to ensure that the design is implementable. However, the high expressivity of OCL makes manual inconsistency checking a difficult task. In addition, the developers cannot detect these problems by OCL itself due to its lack of automated reasoning support. We investigate an approach to translating OCL invariants into OWL 2 DL axioms. We do this where the OCL expression contained in an invariant is converted to the corresponding OWL 2 DL class expression in a compositional way. Our approach covers the OCL expressions including four: PrimaryExp, RelationalExp, LogicalExp and IfExp types. Considering the distinction between the CWA and OWA, we achieve correct translation from RelationalExp using closure axiom. Also, we employ an ontology design pattern to overcome the limitations of OWL 2 DL expressivity when translating IfExp. Then inconsistency checking is done through description logic reasoning by the OWL 2 DL high-performance reasoner. We construct an inductive proof to establish the correctness of our translation approach. Moreover, we evaluate our approach using the implemented TUCO tool prototype.

With the proliferation of location-aware devices, trajectory data have been widely collected, published, and analyzed in real-life applications. However, published trajectory data often contain sensitive attributes, so an attacker who can identify an individual from such data through record linkage, attribute linkage, or similarity attacks can gain sensitive information about this individual. To resist from these attacks, we propose a scheme called Data Privacy Preservation with Perturbation (DPPP). To protect the privacy of sensitive information, we first determine those critical location sequences that can identify specific individuals. Then we perturb these sequences by adding or deleting some moving points while ensuring the published data satisfy (l, α, β)-privacy, an enhanced privacy model from ldiversity. Our experiments on both synthetic and real-life datasets suggest that DPPP achieves better privacy while still ensuring high utility, compared with existing privacy preservation schemes on trajectory.

Local differential privacy (LDP), where each user perturbs her data locally before sending to an untrusted data collector, is a new and promising technique for privacy-preserving distributed data collection. The advantage of LDP is to enable the collector to obtain accurate statistical estimation on sensitive user data (e.g., location and app usage) without accessing them. However, existing work on LDP is limited to simple data types, such as categorical, numerical, and set-valued data. To the best of our knowledge, there is no existing LDP work on key-value data, which is an extremely popular NoSQL data model and the generalized form of set-valued and numerical data. In this paper, we study this problem of frequency and mean estimation on key-value data by first designing a baseline approach PrivKV within the same "perturbation-calibration" paradigm as existing LDP techniques. To address the poor estimation accuracy due to the clueless perturbation of users, we then propose two iterative solutions PrivKVM and PrivKVM+ that can gradually improve the estimation results through a series of iterations. An optimization strategy is also presented to reduce network latency and increase estimation accuracy by introducing virtual iterations in the collector side without user involvement. We verify the correctness and effectiveness of these solutions through theoretical analysis and extensive experimental results.

With the proliferation of cloud computing and data-as-a-service (DaaS), more and more organizations and individuals outsource their data to a third-party service provider. While enjoying the benefits of cloud-based data outsourcing, the data owners are at the risk of losing control of data integrity and access management. In this demonstration, we present a system called vABS, which enables verifiable Attribute-Based Search over shared cloud data. The vABS system adopts the common DaaS architecture, in which the server provides search services to users on behalf of data owners. By employing a novel zero-knowledge approach proposed in our prior work [1], vABS not only provides users with good search experiences, but also supports authenticated query processing with fine-grained access control, which is crucial to many high-security applications.

With data growing exponentially, more and more people prefer to share data with others by storing the data in edge servers. However, edge server cannot be deemed completely trustable as the sensitive data my be disclosed. Therefore, in this paper, we propose an efficient and secure data sharing scheme for edge storage by employing Ciphertext-Policy Attribute-Based Encryption (CP-ABE) which can be utilized to conduct fine-grained control. This scheme can not only support data recovery when some edge servers break down by employing Secret Sharing Scheme, but also can support semi-trusted third party authority via employing re-encryption method. That is, the third party authority can not either reveal the private data stored in edge servers. Finally, we analyze security of our scheme to demonstrate that this scheme is resistant to eavesdropping attack and colluding attack. Additionally, relevant experiments results are shown that the scheme is feasibility and efficiency.

Machine learning models trained by large volume of proprietary data and intensive computational resources are valuable assets of their owners, who merchandise these models to third-party users through prediction service API. However, existing literature shows that model parameters are vulnerable to extraction attacks which accumulate a large number of prediction queries and their responses to train a replica model. As countermeasures, researchers have proposed to reduce the rich API output, such as hiding the precise confidence level of the prediction response. Nonetheless, even with response being only one bit, an adversary can still exploit fine-tuned queries with differential property to infer the decision boundary of the underlying model. In this paper, we propose boundary differential privacy (\(\epsilon \)-BDP) as a solution to protect against such attacks by obfuscating the prediction responses near the decision boundary. \(\epsilon \)-BDP guarantees an adversary cannot learn the decision boundary by a predefined precision no matter how many queries are issued to the prediction API. We design and prove a perturbation algorithm called boundary randomized response that can achieve \(\epsilon \)-BDP. The effectiveness and high utility of our solution against model extraction attacks are verified by extensive experiments on both linear and non-linear models.

Splicing and copy-move are two well-known methods of image tampering, while detection of image splicing and copy-move forgery is an important research topic in image forensics. In this paper, a method based on convolutional neural network with global average pooling was proposed for splicing and copy-move tampering detection. To detect image tampering, the inconsistency between the authentic images and the tampered images should be captured regardless of the image contents. So, the existing strategy using high-pass filter in SRM as initialization of the first layer was improved to reduce the influence of image content and make the features more diverse on each channel at the same time. In order to reduce the number of parameters in the fully connected layers and avoid overfitting, global average pooling was utilized before fully connected layers in the proposed model. Experiments on three public image tampering datasets demonstrated that the proposed method outperformed some state-of-the-art methods.

A scientific environmental investment prediction plays a crucial role in controlling environmental pollution and avoiding the blind investment of environmental management. However effective environmental investment prediction usually has to fact three challenges about diversiform indicators, insufficient data, and the reliability of prediction models. In the present study, a new prediction model is proposed using the extended belief rule-based system (EBRBS) and evidential reasoning (ER) rule, called ensemble EBRBS model, with the aim to overcome the above challenges for better environmental investment prediction. The proposed ensemble EBRBS model consists of two components: 1) multiple EBRBSs, which are constructed on the basis of not only using various feature selection methods to select representative indicators but also data increment transformation to enrich the training data; 2) an ER rule-based combination method, which utilizes the ER rule to accommodate the weights and reliabilities of different EBRBSs with the predicted outputs of these EBRBSs to have an integrated environmental investment prediction. A detailed case study is then provided for validating the proposed model via extensive experimental and comparison analysis based on the real-world environmental data about 25 environmental indicators for 31 provinces in China ranged from 2005 to 2018. The results demonstrate that the ensemble EBRBS model can be used as an effective model to accurately predict environmental investments. More importantly, the ensemble EBRBS model not only obtains a high accuracy better than some existing prediction models, but also has an excellent robustness compared with others under the situations of excessive indicators and insufficient data.

One key challenge for data owners to host their databases in the cloud is data privacy. In this paper, we first demonstrate that even with the most recent hardware-based security technology such as Intel SGX, a hypervisor can still sniff key database operations running in its guest virtual machine (VM) such as the frequency and type of SQL queries, by monitoring the access pattern of this VM’s main and secondary memory. To ensure security against such access pattern monitoring attacks, we then propose ProDB, a minimal adaptation of a conventional DBMS with both hardware enclave and Oblivious RAM protocol. To enhance its performance for practical use, we also design a SQL-aware Path ORAM protocol called SaP ORAM, which optimizes the classic Path ORAM protocol under practical database workload. Through security analysis and extensive experimental results, we prove and show ProDB achieves high security and throughput on commodity cloud hosting servers.

In image classification, can sparse representation (SR) associate one test image with all training ones from the correct class, but not associate with any training ones from the incorrect classes? The backward sparse representation (bSR) which contains complementary information in an opposite direction can remedy the imperfect associations discovered by the general forward sparse representation (fSR). Unfortunately, this complementarity between the fSR and the bSR has not been studied in face recognition. There are two key problems to be solved. One is how to produce additional bases for the bSR. In face recognition, there is no other bases than the single test face image itself for the bSR, which results in large reconstruction residual and weak classification capability of the bSR. The other problem is how to deal with the robustness of the bSR to image corruption. In this paper, we introduce a CoSR model, which combines the fSR and the bSR together, into robust face recognition, by proposing two alternative methods to these two key problems: learning bases and unknown faces help to enrich the bases set of the bSR. Thereby, we also propose two improved algorithms of the CoSR for robust face recognition. Our study shows that our CoSR algorithms obtain inspiring and competitive recognition rates, compared with other state-of-the-art algorithms. The bSR with the proposed methods enriching the bases set contributes the most to the robustness of our CoSR algorithm, and unknown faces works better than learned bases. Moreover, since our CoSR model is performed in a subspace with a very low dimensionality, it gains an overwhelming advantage on time consumption over the traditional RSR algorithm in image pixel space. In addition, our study also reveals that the sparsity plays an important role in our CoSR algorithm for face recognition. HighlightsIt is the first time to explore to compensate the general SR for face recognition.We explore the complementary between fSR and bSR for face recognition.We propose two methods robust to corruption in faces, to expand the bases of bSR.The CoSR is introduced into face recognition, obtaining very competitive performance.

Phase-change memory (PCM), as one of the most promising next-generation memory technologies, offers various attractive properties such as non-volatility, byte addressability, bit alterability, and low idle energy consumption. Recently, PCM has drawn much attention from the database community for optimizing query and transaction performance. As a complement to existing work, we present PCMLogging, a novel logging scheme that exploits PCM for both data caching and transaction logging to minimize I/O accesses in disk-based databases. Specifically, PCMLogging caches dirty pages/records in PCM and further maintains an implicit log in the cached updates to support database recovery. By integrating log and cached updates, PCMLogging enables simplified recovery and prolongs PCM lifetime. Furthermore, using PCMLogging, we develop a wear-leveling algorithm, that evenly distributes the write traffic across the PCM storage space, and a cost-based destaging algorithm that adaptively migrates cached data from PCM to external storage. Compared to classical write-ahead logging (WAL), our trace-driven simulation results reveal up to 1 $\sim$ 20X improvement in system throughput.