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Deep Learning (DL) techniques allow ones to train models from a dataset to solve tasks. DL has attracted much interest given its fancy performance and potential market value, while security issues are amongst the most colossal concerns. However, the DL models may be prone to the membership inference attack, where an attacker determines whether a given sample is from the training dataset. Efforts have been made to hinder the attack but unfortunately, they may lead to a major overhead or impaired usability. In this paper, we propose and implement DAMIA, leveraging Domain Adaptation (DA) as a defense aginist membership inference attacks. Our observation is that during the training process, DA obfuscates the dataset to be protected using another related dataset, and derives a model that underlyingly extracts the features from both datasets. Seeing that the model is obfuscated, membership inference fails, while the extracted features provide supports for usability. Extensive experiments have been conducted to validates our intuition. The model trained by DAMIA has a negligible footprint to the usability. Our experiment also excludes factors that may hinder the performance of DAMIA, providing a potential guideline to vendors and researchers to benefit from our solution in a timely manner.

The development of Industrial Internet of Things (IIoT) provides massive abundant data resources for trading and mining. However, the existing data trading schemes achieve data usage control at the cost of high latency, thereby resulting in poor service quality as the value of IIoT data degrade over time. This paper proposes a monitor-based usage control model to enforce data usage policies on the user side, which eliminates frequent interactions between owners and users. Based on that, a Data Trading Scheme with efficient usage control for IIoT (called DTSI) is devised, which utilizes blockchain and SGX to enable owners to fully control users' identities and operations at minimal overhead. Security analysis shows that DTSI effectively prevents data abuse and ensures the fair exchange of data. Meanwhile, extensive experiments are conducted on the DTSI prototype with real-world IIoT datasets, which demonstrates the efficiency of DTSI by comparing with the state-of-the-art schemes.

With the increasing number of traffic accidents and terrorist attacks by modern vehicles, vehicular digital forensics (VDF) has gained significant attention in identifying evidence from the related digital devices. Ensuring the law enforcement agency to accurately integrate various kinds of data is a crucial point to determine the facts. However, malicious attackers or semi-honest participants may undermine the digital forensic procedures. Enabling accountability and privacy-preservation while providing secure data access control in VDF is a non-trivial challenge. To mitigate this issue, in this paper, we propose a blockchain-based decentralized solution for VDF named BB-VDF, in which the accountable protocols and privacy-preserving algorithm are constructed. The desirable security properties and fine-grained data access control are achieved based on smart contract and the customized cryptographic construction. Specifically, we design a distributed key-policy attribute based encryption scheme with partially hidden access structures, named DKP-ABE-H, to realize the secure fine-grained forensics data access control. Further, a novel smart contract is designed to model the forensics procedures as a finite state machine, which guarantees accountability that each participant performs auditable cooperation under tamper-resistant and traceable transactions. Systematic security analysis and extensive experimental results show the feasibility and practicability of our proposed BB-VDF scheme.

Mobile cloud storage (MCS) provides clients with convenient cloud storage service. In this paper, we propose an efficient, secure and privacy-preserving mobile cloud storage scheme, which protects the data confidentiality and privacy simultaneously, especially the access pattern. Specifically, we propose an oblivious selection and update (OSU) protocol as the underlying primitive of the proposed mobile cloud storage scheme. OSU is based on onion additively homomorphic encryption with constant encryption layers and enables the client to obliviously retrieve an encrypted data item from the cloud and update it with a fresh value by generating a small encrypted vector, which significantly reduces the client's computation as well as the communication overheads. Compared with previous works, our presented work has valuable properties, such as fine-grained data structure (small item size), lightweight client-side computation (a few of additively homomorphic operations) and constant communication overhead, which make it more suitable for MCS scenario. Moreover, by employing the "verification chunks" method, our scheme can be verifiable to resist malicious cloud. The comparison and evaluation indicate that our scheme is more efficient than existing oblivious storage solutions with the aspects of client and cloud workloads, respectively.

Vehicular networks have tremendous potential to improve the road safety and traffic efficiency, and the adoption of the Space-Air-Ground Integrated Network (SAGIN) architecture in vehicular networks can greatly improve the performance of vehicular networks by leveraging the respective advantages of the space, air, and ground segments on coverage, flexibility, reliability, and availability, which results in Space-Air-Ground Integrated Vehicular Networks (SAGIVNs). Trust management is an important tool for constructing trustworthy SAGIVNs, and privacy preservation is also a primary concern in SAGIVNs. They have conflicting requirements and a satisfactory balance between them is urgently required. In this article, we propose a novel Privacy-Preserving Trust Management (PPTM) scheme for the emergency message dissemination in SAGIVNs. The proposed scheme can realize precise trust management and strong conditional privacy preservation simultaneously with low communication overhead, and can provide strong applicability, strong robustness, and multiple other attractive features. Furthermore, the exhaustive theoretical analysis and simulation evaluation are detailed. The results reveal that the proposed scheme is significantly superior to the existing schemes in several aspects.

With the popularization of the geo-positioning technologies and Location Based Service (LBS), the spatial range query has gained increasing attention in VANET. To eliminate local data storage and computation burden, the data owner outsources his/her spatial data and the processing of range query tasks to location based service providers. However, outsourcing tasks may bring the risk of privacy leakage as location based service providers and clients, including data owner and data users are not in the same trusted domain. Besides, malicious service providers may forge or tamper retrieval results due to interest incentives such as saving storage or computational capacity. Therefore, we first propose and construct the Verifiable Spatial Range Query (VSRQ) scheme in VANET by using the hierarchical cell based encoding method, G-tree and the accumulator. To guarantee the correctness and completeness of search results, we leverage an accumulator-based technology. Then, we develop an improved G-tree to accelerate query processing and extend it to support multi-dimensional range queries. Security analysis shows that basic VSRQ and extension of VSRQ schemes are secure in terms of the privacy of index and confidentiality of spatial data. Extensive experiments further show that, basic (or extension of) VSRQ scheme can achieve result integrity while supporting spatial range query and lower computational and storage burden compared with existing schemes.

Industrial automation and control systems (IACS) are tremendously employing supervisory control and data acquisition (SCADA) network. However, their integration into IACS is vulnerable to various cyber-attacks. In this article, we first present population extremal optimization (PEO)-based deep belief network detection method (PEO-DBN) to detect the cyber-attacks of SCADA-based IACS. In PEO-DBN method, PEO algorithm is employed to determine the DBN's parameters, including number of hidden units and the size of mini-batch and learning rate, as there is no clear knowledge to set these parameters. Then, to enhance the performance of single method for cyber-attacks detection, the ensemble learning scheme is introduced for aggregation of the proposed PEO-DBN method, called EnPEO-DBN. The proposed detection methods are evaluated on gas pipeline system dataset and water storage tank system dataset from SCADA network traffic by comparing with some existing methods. Through performance analysis, simulation results show the superiority of PEO-DBN and EnPEO-DBN.

Vehicular networks have tremendous potential to improve road safety, traffic efficiency, and driving comfort, where cooperative vehicular safety applications are a significant branch. In cooperative vehicular safety applications, through the distributed data fusion for large amounts of data from multiple nearby vehicles, each vehicle can intelligently perceive the surrounding conditions beyond the capability of its own onboard sensors. Trust evaluation and privacy preservation are two primary concerns for facilitating the distributed data fusion in cooperative vehicular safety applications. They have conflicting requirements and a good balance between them is urgently needed. Meanwhile, the computation, communication, and storage overheads will all influence the applicability of a candidate scheme. In this paper, we propose a Lightweight Privacy-Preserving Trust Evaluation (LPPTE) scheme which can primely balance the trust evaluation and privacy preservation with low overheads for facilitating the distributed data fusion in cooperative vehicular safety applications. Furthermore, we provide exhaustive theoretical analysis and simulation evaluation for the LPPTE scheme, and the results demonstrate that the LPPTE scheme can obviously improve the accuracy of fusion results and is significantly superior to the state-of-the-art schemes in multiple aspects.

The great development of smart networks enables Internet of Vehicles (IoV) as a promising paradigm to provide pervasive services, where privacy issues for location-based services (LBSs) have attracted considerable attention. In terms of location privacy, inspired by differential privacy, geo-indistinguishability (Geo-Ind) has recently become a prevalent privacy model for LBSs. Although Geo-Ind guarantees the location privacy, users’ other privacy concerns are still at risk if the location perturbation behavior is exposed due to implausible reported locations. Through experiments we find the probability that the classical Geo-Ind mechanism perturbs the true location to implausible areas can be more than 50%. To address it, we first propose an enhanced privacy definition beyond Geo-Ind, called Perturbation-Hidden, to prevent location perturbation behaviors of users from being recognized by guaranteeing their pseudo-locations plausible. Then we design a mechanism to achieve this definition by transplanting the differential private exponential mechanism to our approach. Furthermore, we propose a method for determining the retrieval area utilizing dynamic programming to ensure the accuracy of LBSs. Finally, we theoretically prove that our mechanism satisfies the privacy definition. Extensive experiments on simulations and a real-world dataset show that our proposal achieves 100% plausible pseudo-locations while ensuring high query precision and recall.

As a potential application field of the sixth-generation (6G) communication technology and a promising part of massive Internet of Things (IoT), vehicular networks have attracted considerable attention from both academia and industry in recent years, where the cooperative safety applications are a significant branch. It is widely acknowledged that 6G is able to provide high-throughput and low-latency wireless communication capability for vehicular networks, support massive interconnectivity in vehicular networks with diverse service requirements, and significantly improve the performance of vehicular networks. Both trust management and privacy preservation play significant roles in vehicular networks, and there exists a tradeoff between them. For providing a satisfactory solution to balance the trust management and privacy preservation in vehicular networks, we put forward a novel scheme named as BTMPP (which is able to provide both the precise trust management and strong conditional privacy preservation simultaneously) in this article by leveraging the famous bloom filter (BF)-based private set intersection (PSI) technology. Furthermore, the theoretical analysis for the correctness, strong conditional privacy preservation capability, strong robustness, precise trust management, and the other features is detailed, and a series of simulations are conducted. The results reveal that the proposed scheme significantly outperforms the existing schemes in several aspects.

The rapid growth of digital images motivates organizations and individuals to outsource image storage and computation to the cloud. However, the defenseless upload will raise the risk of privacy leakage while the simple encryption would impede the efficient usage of data. In this paper, we propose a privacy-preserving image retrieval scheme, in which the images are encrypted but similar images to a query can be efficiently retrieved from the encrypted images. Specifically, the image content is protected by big-block permutation, $3 \times 3$ block permutation within big-blocks, pixel permutation within $3 \times 3$ blocks, and polyalphabetic cipher. The use of polyalphabetic cipher improves security and causes no degradation in terms of retrieval accuracy as the substitution tables are generated by the order-preserving encryption. In this way, secure Local Binary Pattern (LBP) features can be directly extracted as the local features from the encrypted big-blocks, which is efficient as there is no communication between the cloud server and image owners to do so. The secure local LBP features are used to generate the feature vector for each image by the bag-of-words model. Finally, the similarity among the encrypted images is measured by the Manhattan distance of such feature vectors. The security analysis and experimental results demonstrate that the proposed scheme outperforms the main existing schemes in terms of security and retrieval accuracy.

Election and voting play crucial roles in democratic society for an elactorate to make a collective decision. E-voting is one of the most challenging problems in cryptographic research to provide multiple dimensions security assurances. In this paper, we study an important voting paradigm, score voting, with privacy protection, which has not been investigated in previous work. We propose a blockchain based self-tallying election system to support score voting, dubbed “PriScore”, where the ballots are recorded on blockchain to prevent vote forgery or tampering. PriScore makes it possible for each voter to assign different evaluation scores (within a certain range) for the candidates as ranked-choice, where the sum of the scores in each ballot should be a predefined constant, and the evaluation scores are encrypted to maintain confidentiality. A major challenge in score voting is to simultaneously prove two constraint conditions: range proof and sum proof. We introduce a new technique, called dual zero-knowledge proof (dual-ZKP), to prove the scores satisfying two crucial requirements, which integrates “1-out-of- $K$ ” proof and distributed ElGamal crypto in a non-trivial way. The self-tallying mechanism in PriScore enables any party in the system to calculate and verify the election result, which provides fairness, dispute-freeness. The security analysis demonstrates that PriScore achieves completeness, soundness, eligibility, universal/individual verifiability and multiple-voting detection. We evaluate the performance of PriScore on modern workbench to test the performance, and also on a blockchain platform to measure the resource consumption. The experiments show that PriScore preserves privacy of score voting with reasonable overheads.

Blockchain has led the trend of decentralized applications and shown great use beyond cryptocurrencies. Decentralized storage such as Storj and Sia leverages blockchain to establish an open platform for sharing economy, which provides private and reliable file-outsourcing services. However, the ubiquitous keyword search function over encrypted files is yet to be supported. To enable this function, we first apply searchable encryption techniques to the decentralized setting. But this primitive can hardly ensure the service integrity. The reason is that decentralized storage commonly faces severe threats from both clients and service peers. Service peers may return partial or incorrect results, while clients may intentionally slander the service peers to avoid payments. To address these threats, we utilize the smart contract to record the logs of encrypted search (aka evidence) on the blockchain, and devise a fair protocol to handle disputes and issue fair payments. Using a dynamic-efficient searchable encryption scheme as an instantiation, we craft a concrete scheme that preserves encrypted search capability and enforces ecosystem healthiness, so that service peers are incentivized to make real efforts and jointly guarantee service reliability. We implement our scheme in Python and Solidity, and test its search performance and transaction costs on Ethereum.

Deep neural networks (DNNs) are easily fooled by adversarial examples. Most existing defense strategies defend against adversarial examples based on full information of whole images. In reality, one possible reason as to why humans are not sensitive to adversarial perturbations is that the human visual mechanism often concentrates on most important regions of images. A deep attention mechanism has been applied in many computer fields and has achieved great success. Attention modules are composed of an attention branch and a trunk branch. The encoder/decoder architecture in the attention branch has potential of compressing adversarial perturbations. In this article, we theoretically prove that attention modules can compress adversarial perturbations by destroying potential linear characteristics of DNNs. Considering the distribution characteristics of adversarial perturbations in different frequency bands, we design and compare three types of attention modules based on frequency decomposition and reorganization to defend against adversarial examples. Moreover, we find that our designed attention modules can obtain high classification accuracies on clean images by locating attention regions more accurately. Experimental results on the CIFAR and ImageNet dataset demonstrate that frequency reorganization in attention modules can not only achieve good robustness to adversarial perturbations, but also obtain comparable, even higher classification, accuracies on clean images. Moreover, our proposed attention modules can be integrated with existing defense strategies as components to further improve adversarial robustness.

Nowadays, the pseudonym swap has become the mainstream technology for protecting vehicles’ trajectory privacy in vehicle ad hoc networks. However, the existing pseudonym swap methods cannot strictly provide the unlinkability between the new pseudonym and old pseudonym of the vehicle due to the lack of theoretical privacy guarantee, resulting in severe leakages of vehicles’ trajectory privacy. Our experiment also proves this point and we find that existing works may cause vehicle’s pseudonyms to be linked with a probability higher than 60% because they always choose two vehicles with very different driving states (e.g., speeds, directions, and positions) to swap their pseudonyms. To solve this issue, we first give a formal privacy definition based on generalized differential privacy, called pseudonym indistinguishability, to provide a strict unlinkability for pseudonym swap. Then, we design an appropriate utility metric and a new pseudonym swap mechanism, which selects a pseudonym for a vehicle by adapting a differential privacy exponential mechanism to satisfy pseudonym indistinguishability. Abstracting from attackers’ prior knowledge, we can strictly guarantee that if two vehicles have a high similarity of driving states, it is impossible for attackers to link the vehicles and their pseudonyms after the swap. Theoretical analyses prove that our mechanism satisfies the proposed privacy definition, thus ensuring the unlinkability between the new pseudonym and the old pseudonym. Extensive experiments on a real data set show that our work only requires about 50% of pseudonym quantities compared to other works and can make the vehicle successfully complete the swap process with a probability of more than 90%, which is higher than any of existing works.

The wide adoption of cloud greatly facilitates the sharing of explosively generated media content today, yet deprives content providers’ direct control over the outsourced media content. Thus, it is pivotal to build an encrypted cloud media center where only authorized access is allowed. Enforcing access control alone, however, cannot fully protect content providers’ interests, as authorized users may later become traitors that illegally redistribute media content to the public. Such realistic threat should have been seriously treated yet is largely overlooked in the literature. In this paper, we initiate the first study on secure media sharing with fair traitor tracing in the encrypted cloud media center, through a new marriage of proxy re-encryption (for secure media sharing) and fair watermarking (for fair traitor tracing). Our key insight is to fully leverage the homomorphic properties residing in proxy re-encryption to embrace operations in fair watermarking. Two protocols are proposed for different application scenarios. We also provide complexity analysis for performance, showing that our work can also be treated as secure outsourcing of fair watermarking, and thus kills two birds with one stone. We thoroughly analyze the security strengths and conduct extensive experiments to validate the effectiveness of our design.

Sample balancing includes sample selection and sample reweighting. Sample selection aims to remove some bad samples that may lead to bad local optima. Sample reweighting aims to assign optimal weights to samples to improve performance. In this article, we integrate a sample selection method based on self-paced learning into deep learning frameworks and study the influence of different sample selection strategies on training deep networks. In addition, most of the existing sample reweighting methods mainly take per-class sample number as a metric, which does not fully consider sample qualities. To improve the performance, we propose a novel metric based on the multiview semantic encoders to reweight the samples more appropriately. Then, we propose an optimization mechanism to embed sample weights into loss functions of deep networks, which can be trained in end-to-end manners. We conduct experiments on the CIFAR data set and the ImageNet data set. The experimental results demonstrate that our proposed sample balancing method can improve the performances of deep learning methods in several visual recognition tasks.

Vehicular ad-hoc networks (VANETs) have recently attracted considerable attention from both industry and academia for improving road safety and traffic efficiency. Trust modeling plays a significant role in VANETs, however, the existing trust models cannot primely conform to the characteristics of VANETs. This article proposes a novel trust cascading-based emergency message dissemination (TCEMD) model which incorporates the entity-oriented trust values into data-oriented trust evaluation in an efficient manner. In the proposed model, when an emergency event (e.g., an obstacle in front of the road) occurs, the emergency messages can be disseminated among the nearby vehicles in a trust cascading manner, where the entity-oriented trust values (which are evaluated and updated by leveraging the trust certificates and are contained in the messages) are adopted as important weights. Subsequently, the theoretical analysis for the robustness against several kinds of attacks and malicious behaviors, failure tolerance features, compatibility for several kinds of special situations, and incentive mechanisms in the TCEMD model are detailed. Afterwards, a series of simulations and analyses are conducted in a typical highway environment, and the results reveal that the proposed model significantly outperforms the existing models in several cases.

Attribute-based encryption (ABE) provides a promising solution for enabling scalable access control over encrypted data stored in the untrusted servers (e.g., cloud) due to its ability to perform data encryption and decryption defined over descriptive attributes . In order to bind different components which correspond to different attributes in a user’s attribute-based decryption key together, key randomization technique has been applied in most existing ABE schemes. This randomization method , however, also empowers a user the capability of regenerating a newly randomized decryption key over a subset of the attributes associated with the original decryption key. Because key randomization breaks the linkage between this newly generated key and the original key, a malicious user could leak the new decryption key to others without taking any responsibility for the key abuse. To solve this problem, we think of key regeneration-free ABE to disallow a user from randomizing his/her decryption key in any manner, i.e., a user can only delegate his/her decryption key in exactly the same form without any modification so that any abused or pirated key can be traced back to its original owner. Motivated by strongly unforgeable signature, we first define a security notion called strong key unforgeability, and show that ABE schemes equipped with the strong key unforgeability are immune to key regeneration. We then provide a generic transformation to convert ciphertext-policy ABE (CP-ABE) schemes of certain type to key regeneration-free CP-ABE schemes, and show how the transformation works by presenting two concrete constructions.

Searchable encryption is an important technique for public cloud storage service to provide user data confidentiality protection and at the same time allow users performing keyword search over their encrypted data. Previous schemes only deal with exact or fuzzy keyword search to correct some spelling errors. In this paper, we propose a new wildcard searchable encryption system to support wildcard keyword queries which has several highly desirable features. First, our system allows multiple keywords search in which any queried keyword may contain zero, one or two wildcards, and a wildcard may appear in any position of a keyword and represent any number of symbols. Second, it supports simultaneous search on multiple data owner's data using only one trapdoor. Third, it provides flexible user authorization and revocation to effectively manage search and decryption privileges. Fourth, it is constructed based on homomorphic encryption rather than Bloom filter and hence completely eliminates the false probability caused by Bloom filter. Finally, it achieves a high level of privacy protection since matching results are unknown to the cloud server in the test phase. The proposed system is thoroughly analyzed and is proved secure. Extensive experimental results indicate that our system is efficient compared with other existing wildcard searchable encryption schemes in the public key setting.

Load frequency control (LFC) is widely employed to keep smart grids stable and secure. This paper proposes an adaptive resilient LFC scheme for sub-systems of smart grids under denial-of-service (DoS) attacks with energy constraint. Firstly, a resilient triggering communication scheme is introduced, where the triggering condition includes the uncertainty item induced by DoS attacks. Secondly, an adaptive resilient event-triggering LFC scheme is proposed to further reduce the communication burden and defeat the DoS attacks, where the event-triggering parameter can be dynamically adjusted. Third, a stability criterion is derived for Proportional Integral-based LFC systems by employing Lyapunov theory. Finally, to validate the proposed control scheme, case studies are carried out based on three different LFC power generation systems including one-area, two-area and three-area models. The simulation results clearly demonstrate that the proposed adaptive resilient LFC scheme can reduce the communication burden while defeating DoS attacks by comparing with resilient event-triggering LFC scheme and event-triggering LFC scheme in term of defense effect and average triggering period.

While enjoying the convenience brought by Location Based Service (LBS), the location privacy of vehicles in VANET may be disclosed. Distributed k -anonymity, as one of the most popular privacy protection methods, fails to take the trustworthiness of participants into account, resulting in malicious tracing of vehicles, which further leads to the sensitive information leakage, and even the safety threat of personal property. To address this issue, we propose a blockchain enabled trust-based location privacy protection scheme in VANET. Specifically, by analyzing the different requirements of the request vehicle and the cooperative vehicle during the process of constructing the anonymous cloaking region, as well as combining the characteristics of these two roles, we devise the trust management method based on Dirichlet distribution , such that both the requester and the cooperator will only cooperate with the vehicles they trust. Moreover, by employing blockchain, we also proposed the data structure to record the trustworthiness of vehicles on publicly available blocks timely, so that any vehicle can access the historical trust information of counterparties whenever necessary. Finally, the construction process of anonymous cloaking region is presented. Security analysis and extensive experiments indicate that the proposal is resilient to various trust model attacks, it can effectively detect the malicious vehicles, and preserve the location privacy of vehicles in the anonymous cloaking region construction, while the required time delay is limited.

In certificateless proxy signature (CLPS), the key generation center is responsible for initializing the system parameters and can obtain the opportunity to adaptively set some trapdoors in them when wanting to launch some attacks. Until now, how to withstand the malicious-but-passive key generation center (MKGC) attacks in CLPS is still an interesting problem. In this paper, we focus on the challenging issue and introduce a CLPS scheme provably secure in the standard model. To the best of our knowledge, we are the first to demonstrate its security under MKGC attacks by adopting the technology of embedding the classic difficulty problems into the target entity public key rather than the system parameters during the security proof process.

Searchable Encryption (SE) enables private queries on encrypted documents. Most existing SE schemes focus on constructing industrial-ready, practical solutions at the expense of information leakages that are considered acceptable. In particular, ShadowCrypt utilizes a cryptographic approach named ''efficiently deployable, efficiently searchable encryption'' (EDESE) that reveals the encrypted dataset and the query tokens among other information. However, recent attacks showed that such leakages can be exploited to (partially) recover the underlying keywords of query tokens under certain assumptions on the attacker's background knowledge. We continue this line of work by presenting LEAP, a new leakage-abuse attack on EDESE schemes that can accurately recover the underlying keywords of query tokens based on partially known documents and the L2 leakage as per defined by Cash et al. (CCS '15). As an auxiliary function, our attack supports document recovery in the similar setting. To the best of our knowledge, this is the first attack on EDESE schemes that achieves keyword recovery and document recovery without error based on partially known documents and L2 leakage. We conduct extensive experiments to demonstrate the effectiveness of our attack by varying levels of attacker's background knowledge.

With more organizational and individual data owners storing their data in the cloud, there is a corresponding need to ensure that such outsourced data can be computed by the data owners or some authorized third parties. In other words, the cloud server performs the requested computation, and returns the computation result to the requesting party. Such a paradigm is referred to as outsourced computation in the literature. A challenge is how one can ensure the correctness of the returned result, and this has been extensively studied in the literature. For example, in 2017, Song et al . proposed a protocol for publicly verifiable computation of polynomials over outsourced data with multiple sources. However, we reveal that a core building block of their protocol allows an adversary to forge the signatures on the outsourced data. Hence, this invalidates the security of the protocol. We also point out their proposal is yet very interesting and useful, only a little improvement can strengthen their proposal to be secure.

Commercial advertisement (ad) dissemination has been proliferating on connected vehicles, allowing users to promote their products via vehicle-to-vehicle/-infrastructure communications. Despite the prospect of ad dissemination in vehicular networks, it faces challenges upon deployment especially on security and privacy. Particularly, vehicles may collude to defraud the advertiser to obtain rewards without disseminating ads, which may cause unfair “free-riding” issue in these activities. Furthermore, concerns on possible privacy leakage may discourage vehicles to participate in the process of ad dissemination. In addition, external DDoS attacks and internal single point of failure may also affect service availability. To address these issues, we explore the potential of blockchain technology to construct a fair and anonymous scheme for advertising in vehicular networks. We first present the overview of the blockchain-based ad dissemination framework. Then, under the framework, we design a concrete, fair and anonymous scheme. To ensure fairness, we utilize the Merkle hash tree together with smart contracts to achieve the “proof-of-ad-receiving” property (i.e., check whether a vehicle indeed receives an ad without deception or introducing significant storage cost) to mitigate the “free-riding” attack. On the other hand, any ad receiver who acquires a dissemination reward per ad more than once can be effectively detected and will be punished which is achieved by using smart contracts. Additionally, the proposed scheme can protect vehicles’ privacy in terms of anonymity and conditional linkability based on zero-knowledge proof techniques. Lastly, extensive security analysis and implementations demonstrate the feasibility and efficiency of the scheme.

Nowadays more and more enterprises prefer to outsource their databases to remote cloud servers, they also want to access/query/update the remote outsourced databases just as accessing/quering/updating on the local database systems. Thus how to ensure the outsourced database being correct/confidential/integral is very important, especially how to ensure the correctness and completeness of the query results are big challenge problems. Aiming at solving these problems, recently Song et al. proposed a public authentication protocol for outsourced databases with multi-user modification, unfortunately in this short paper we show their proposal is not secure.

Specialized hardware accelerators, particularly those that are programmable and flexible to target multiple problems in their domain, have proven to provide orders of magnitude speedup and energy efficiency. However, their design requires extensive manual effort, due to the need for hardware-software codesign to balance the degree and forms of specialization to the domains or program behaviors of interest. This article provides the first steps towards one approach for automating much of these processes. The insight behind our work is to recognize that decoupled spatial architectures both define a rich design space with many tradeoffs for different kinds of applications, and also can be composed out of a simple set of well-defined primitives. Therefore, we propose a modular accelerator design framework, DAEGEN, a.k.a. Decoupled Access Excution Accelerator Generator. This article defines an initial compiler and architecture primitives, and we discuss key challenges.

Crowdsourcing systems which utilize the human intelligence to solve complex tasks have gained considerable interest and adoption in recent years. However, the majority of existing crowdsourcing systems rely on central servers, which are subject to the weaknesses of traditional trust-based model, such as single point of failure. They are also vulnerable to distributed denial of service (DDoS) and Sybil attacks due to malicious users involvement. In addition, high service fees from the crowdsourcing platform may hinder the development of crowdsourcing. How to address these potential issues has both research and substantial value. In this paper, we conceptualize a blockchain-based decentralized framework for crowdsourcing named CrowdBC, in which a requester's task can be solved by a crowd of workers without relying on any third trusted institution, users’ privacy can be guaranteed and only low transaction fees are required. In particular, we introduce the architecture of our proposed framework, based on which we give a concrete scheme. We further implement a software prototype on Ethereum public test network with real-world dataset. Experiment results show the feasibility, usability, and scalability of our proposed crowdsourcing system.

We propose a construction of the modulus of the RSA public keys for decentralized public-key trust generation like Web-of-Trust, where the modulus has inside structure. The first function of the structure is that it enables a peer to verify that the owner of the public key certainly generated the corresponding secret key. The second function is to tie up the public key with the owner’s identity as well as her guarantors identities. Our construction is based on the modified version of the Lenstra’s algorithm, by which a related second public key and the identity strings are embedded into the RSA modulus. The second public key is of the elliptic-curve cryptosystem (ECC) which has almost equivalent security level to the RSA cryptosystem. Our construction is applicable to a peer-to-peer network equipped with the blockchain of “consortium” and “trusted” type to yield a flat network that does not depends on PKI of the X.509 type.

With the ever-increasing requirement of storage and computation resources, it is unrealistic for local devices (with limited sources) to implement large-scale data processing. Therefore, individuals or corporations incline to outsource their computation requirements to the cloud. However, data outsourcing brings security and privacy concerns to users when the cloud servers are not fully trusted. Recently, extensive research works are conducted, aiming at secure outsourcing schemes for diverse computational tasks via different technologies. In this survey, we provide a technical review and comparison of existing outsourcing schemes using diverse secure computation methods. Specifically, we begin the survey by describing security threats and requirements of secure outsourcing computation. Meanwhile, we introduce four secure techniques (i.e., secure multi-party computation, pseudorandom functions, software guard extensions, and perturbation approaches) and their related works. Then, we focus on the theories and evolution of homomorphic encryption, as well as the applications of the basic operations and application-specific tasks. Finally, we discuss the security and performance of existing works and give future directions in this field.

Data holders, such as mobile apps, hospitals and banks, are capable of training machine learning (ML) models and enjoy many intelligence services. To benefit more individuals lacking data and models, a convenient approach is needed which enables the trained models from various sources for prediction serving, but it has yet to truly take off considering three issues: (i) incentivizing prediction truthfulness; (ii) boosting prediction accuracy; (iii) protecting model privacy. We design FedServing, a federated prediction serving framework, achieving the three issues. First, we customize an incentive mechanism based on Bayesian game theory which ensures that joining providers at a Bayesian Nash Equilibrium will provide truthful (not meaningless) predictions. Second, working jointly with the incentive mechanism, we employ truth discovery algorithms to aggregate truthful but possibly inaccurate predictions for boosting prediction accuracy. Third, providers can locally deploy their models and their predictions are securely aggregated inside TEEs. Attractively, our design supports popular prediction formats, including top-1 label, ranked labels and posterior probability. Besides, blockchain is employed as a complementary component to enforce exchange fairness. By conducting extensive experiments, we validate the expected properties of our design. We also empirically demonstrate that FedServing reduces the risk of certain membership inference attack.

Because of the increasing demand for computation in DNN, researchers develope both hardware and software mechanisms to reduce the compute and memory burden. A widely adopted approach is to use mixed precision data types. However, it is hard to leverage mixed precision without hardware support because of the overhead of data casting. Hardware vendors offer tensorized instructions for mixed-precision tensor operations, like Intel VNNI, Tensor Core, and ARM-DOT. These instructions involve a computing idiom that reduces multiple low precision elements into one high precision element. The lack of compilation techniques for this makes it hard to utilize these instructions: Using vendor-provided libraries for computationally-intensive kernels is inflexible and prevents further optimizations, and manually writing hardware intrinsics is error-prone and difficult for programmers. Some prior works address this problem by creating compilers for each instruction. This requires excessive effort when it comes to many tensorized instructions. In this work, we develop a compiler framework to unify the compilation for these instructions -- a unified semantics abstraction eases the integration of new instructions, and reuses the analysis and transformations. Tensorized instructions from different platforms can be compiled via UNIT with moderate effort for favorable performance. Given a tensorized instruction and a tensor operation, UNIT automatically detects the applicability, transforms the loop organization of the operation,and rewrites the loop body to leverage the tensorized instruction. According to our evaluation, UNIT can target various mainstream hardware platforms. The generated end-to-end inference model achieves 1.3x speedup over Intel oneDNN on an x86 CPU, 1.75x speedup over Nvidia cuDNN on an NvidiaGPU, and 1.13x speedup over a carefully tuned TVM solution for ARM DOT on an ARM CPU., 13 pages, 13 figures, and 1 table

ML-as-a-service (MLaaS) becomes increasingly popular and revolutionizes the lives of people. A natural requirement for MLaaS is, however, to provide highly accurate prediction services. To achieve this, current MLaaS systems integrate and combine multiple well-trained models in their services. Yet, in reality, there is no easy way for MLaaS providers, especially for startups, to collect sufficiently well-trained models from individual developers, due to the lack of incentives. In this paper, we aim to fill this gap by building up a model marketplace, called as Golden Grain, to facilitate model sharing, which enforces the fair model-money swapping process between individual developers and MLaaS providers. Specifically, we deploy the swapping process on the blockchain, and further introduce a blockchain-empowered model benchmarking process for transparently determining the model prices according to their authentic performances, so as to motivate the faithful contributions of well-trained models. Especially, to ease the blockchain overhead for model benchmarking, our marketplace carefully offloads the heavy computation and designs a secure off-chain on-chain interaction protocol based on a trusted execution environment (TEE), for ensuring both the integrity and authenticity of benchmarking. We implement a prototype of our Golden Grain on the Ethereum blockchain, and conduct extensive experiments using standard benchmark datasets to demonstrate the practically affordable performance of our design.

Bluetooth Low Energy (BLE) is a widely adopted wireless communication technology in the Internet of Things (IoT). BLE offers secure communication through a set of pairing strategies. However, these pairing strategies are obsolete in the context of IoT. The security of BLE based devices relies on physical security, but a BLE enabled IoT device may be deployed in a public environment without physical security. Attackers who can physically access a BLE-based device will be able to pair with it and may control it thereafter. Therefore, manufacturers may implement extra authentication mechanisms at the application layer to address this issue. In this paper, we design and implement a BLE Security Scan (BLESS) framework to identify those BLE apps that do not implement encryption or authentication at the application layer. Taint analysis is used to track if BLE apps use nonces and cryptographic keys, which are critical to cryptographic protocols. We scan 1073 BLE apps and find that 93% of them are not secure. To mitigate this problem, we propose and implement an application-level defense with a low-cost $0.55 crypto co-processor using public key cryptography.

Domain-specific hardware accelerators can provide orders of magnitude speedup and energy efficiency over general purpose processors. However, they require extensive manual effort in hardware design and software stack development. Automated ASIC generation (eg. HLS) can be insufficient, because the hardware becomes inflexible. An ideal accelerator generation framework would be automatable, enable deep specialization to the domain, and maintain a uniform programming interface. Our insight is that many prior accelerator architectures can be approximated by composing a small number of hardware primitives, specifically those from spatial architectures. With careful design, a compiler can understand how to use available primitives, with modular and composable transformations, to take advantage of the features of a given program. This suggests a paradigm where accelerators can be generated by searching within such a rich accelerator design space, guided by the affinity of input programs for hardware primitives and their interactions. We use this approach to develop the DSAGEN framework, which automates the hardware/software co-design process for reconfigurable accelerators. For several existing accelerators, our evaluation demonstrates that the compiler can achieve 89% of the performance of manually tuned versions. For automated design space exploration, we target multiple sets of workloads which prior accelerators are design for; the generated hardware has mean 1.3 x perf2/mm2 over prior programmable accelerators.

Dense linear algebra kernels are critical for wireless, and the oncoming proliferation of 5G only amplifies their importance. Due to the inductive nature of many such algorithms, parallelism is difficult to exploit: parallel regions have fine-grain producer/consumer interaction with iteratively changing depen-dence distance, reuse rate, and memory access patterns. This makes multi-threading impractical due to fine-grain synchronization, and vectorization ineffective due to the non-rectangular iteration domain. CPUs, DSPs, and GPUs perform order-of-magnitude below peak. Our insight is that if the nature of inductive dependences and memory accesses were explicit in the hardware/software interface, then a spatial architecture could efficiently execute parallel code regions. To this end, we first develop a novel execution model, inductive dataflow, where inductive dependence patterns and memory access patterns (streams) are first-order primitives. Second, we develop a hybrid spatial architecture combining systolic and tagged dataflow execution to attain high utilization at low energy and area cost. Finally, we create a scalable design through a novel vector-stream control model which amortizes control overhead both in time and spatially across architecture lanes. We evaluate our design, REVEL, with a full stack (compiler, ISA, simulator, RTL). Across a suite of linear algebra kernels, REVEL outperforms equally-provisioned DSPs by 4.6×-37×. Compared to state-of-the-art spatial architectures, REVEL is mean 3× faster. Compared to a set of ASICs, REVEL is only 2× the power and half the area.

In this paper, we propose a privacy-preserving medical treatment system using nondeterministic finite automata (NFA), hereafter referred to as P-Med, designed for the remote medical environment. P-Med makes use of the nondeterministic transition characteristic of NFA to flexibly represent the medical model, which includes illness states, treatment methods and state transitions caused by exerting different treatment methods. A medical model is encrypted and outsourced to the cloud to deliver telemedicine services. Using P-Med, patient-centric diagnosis and treatment can be made on-the-fly while protecting the confidentiality of a patient's illness states and treatment recommendation results. Moreover, a new privacy-preserving NFA evaluation method is given in P-Med to get a confidential match result for the evaluation of an encrypted NFA and an encrypted data set, which avoids the cumbersome inner state transition determination. We demonstrate that P-Med realizes treatment procedure recommendation without privacy leakage to unauthorized parties. We conduct extensive experiments and analyses to evaluate efficiency., Comment: The full version of the paper accepted by IEEE Transactions on Cloud Computing

Transport Layer Security Inspection (TLSI) enables enterprises to decrypt, inspect and then re-encrypt users’ traffic before it is routed to the destination. This breaks the end-to-end security guarantee of the TLS specification and implementation. It also raises privacy concerns since users’ traffic is now known by the enterprises, and third-party middlebox providers providing the inspection services may additionally learn the inspection or attack rules, policies of the enterprises. Two recent works, BlindBox (SIGCOMM 2015) and PrivDPI (CCS 2019) propose privacy-preserving approaches that inspect encrypted traffic directly to address the privacy concern of users’ traffic. However, BlindBox incurs high preprocessing overhead during TLS connection establishment, and while PrivDPI reduces the overhead substantially, it is still notable compared to that of TLSI. Furthermore, the underlying assumption in both approaches is that the middlebox knows the rule sets. Nevertheless, with the services increasingly migrating to third-party cloud-based setting, rule privacy should be preserved. Also, both approaches are static in nature in the sense that addition of any rules requires significant amount of preprocessing and re-instantiation of the protocols.

Searchable Encryption (SE) enables a user to search over encrypted data, such as data stored in a remote cloud server. Existing certificate-, identity-, and attribute-based SE schemes suffer from certificate management or key escrow limitations. Furthermore, the semi-honest-but-curious cloud may conduct partial search operations and return a fraction of the search results (i.e., incomplete results) in order to reduce costs. In this paper, we present a secure cryptographic primitive, Verifiable Multiple Keywords Search (VMKS) over ciphertexts, which leverages the Identity-Based Encryption (IBE) and certificateless signature techniques. The VMKS scheme allows the user to verify the correctness of search results and avoids both certificate management or key escrow limitations. We then demonstrate the security of proposed VMKS scheme (i.e., the scheme achieves both ciphertext indistinguishability and signature unforgeability). We also use a real-world dataset to evaluate its feasibility and efficiency.

Certificate-based encryption (CBE) is a very useful cryptographic primitive which not only simplifies the certificate management in traditional public-key encryption, but also solves the key escrow problem inherent in identity-based encryption. How to construct a provably-secure CBE scheme without using random oracles has been attracting the attentions of the research community. Recently, Lu et al. introduced a CBE scheme and claimed that their scheme is secure against adaptive chosen ciphertext attacks even considering a malicious certification authority (CA). In this paper, we demonstrate that a chosen ciphertext attacker can easily distinguish the challenge ciphertext generated by the challenger according to their security model. Further, the CA can trivially decrypt any entity’s ciphertext without knowing the entity’s secret key. In addition, we also point out that their security proof has some flaws and give a new CBE scheme secure against malicious CA attacks in the standard model.

With the increasing popularity of cloud-based data services, data owners are highly motivated to store their huge amount of potentially sensitive personal data files on remote servers in encrypted form. Clients later can query over the encrypted database to retrieve files while protecting privacy of both the queries and the database, by allowing some reasonable leakage information. To this end, the notion of searchable symmetric encryption (SSE) was proposed. Meanwhile, recent literature has shown that most dynamic SSE solutions leaking information on updated keywords are vulnerable to devastating file-injection attacks. The only way to thwart these attacks is to design forward-private schemes. In this paper, we investigate new privacy-preserving indexing and query processing protocols which meet a number of desirable properties, including the multi-keyword query processing with conjunction and disjunction logic queries, practically high privacy guarantees with adaptive chosen keyword attack (CKA2) security and forward privacy, the support of dynamic data operations, and so on. Compared with previous schemes, our solutions are highly compact, practical, and flexible. Their performance and security are carefully characterized by rigorous analysis. Experimental evaluations conducted over a large representative data set demonstrate that our solutions can achieve modest search time efficiency, and they are practical for use in large-scale encrypted database systems.

Smart contract technology is reshaping conventional industry and business processes. Being embedded in blockchains, smart contracts enable the contractual terms of an agreement to be enforced automatically without the intervention of a trusted third party. As a result, smart contracts can cut down administration and save services costs, improve the efficiency of business processes and reduce the risks. Although smart contracts are promising to drive the new wave of innovation in business processes, there are a number of challenges to be tackled.This paper presents a survey on smart contracts. We first introduce blockchains and smart contracts. We then present the challenges in smart contracts as well as recent technical advances. We also compare typical smart contract platforms and give a categorization of smart contract applications along with some representative examples., 19 pages, 8 figures

Balise is a popular wayside device to provide accurate location information for subway station parking by sending telegrams to passing trains. By craftily disturbing wireless signals of balise telegrams, this paper proposes three attacks that may make passengers fall and even cause injury. Concretely, the first attack is to jam telegrams such that balises cannot be detected by a passing train; the second attack changes the location of transmitting telegrams by jamming and replaying; and the third attack is to change the total time of transmitting telegrams. All the attacks exploit the train localization mechanism such that a passing train localizes its position inaccurately and then takes improper control actions. Furthermore, since these attacks are independent, they can be launched at the same time to achieve advanced attacks. As the attacks do not require to tamper with the balises, they can be launched easily. Our simulations demonstrate the effectiveness of the proposed attacks. To defeat these attacks, the received telegrams need be verified by a train based on fidelity of telegram data.

Millions of private images are generated in various digital devices every day. The consequent massive computational workload makes people turn to cloud computing platforms for their economical computation resources. Meanwhile, the privacy concerns over the sensitive information contained in outsourced image data arise in public. In fact, once uploaded to cloud, the security and privacy of the image content can only presume upon the reliability of the cloud service providers. Lack of assuring security and privacy guarantees becomes the main barrier to further deployment of cloud-based image processing systems. This paper studies the design targets and technical challenges lie in constructing cloud-based privacy-preserving image processing system. We explore various image processing tasks, including image feature detection, digital watermarking, content-based image search. The state-of-the-art techniques, including secure multiparty computation, and homomorphic encryption are investigated. A detailed taxonomy of the problem statement and the corresponding solutions is provided

In this paper, we propose a framework for efficient and privacy-preserving outsourced calculation of rational numbers, which we refer to as POCR. Using POCR, a user can securely outsource the storing and processing of rational numbers to a cloud server without compromising the security of the (original) data and the computed results. More specifically, we present a Paillier cryptosystem with threshold decryption (PCTD), the core cryptographic primitive, to reduce the private key exposure risk in POCR. We also present the tool kits required in the privacy preserving calculation of integers and rational numbers to ensure that commonly used outsourced operations can be handled on-the-fly. We then prove that the proposed POCR achieves the goal of secure integer and rational number calculation without resulting in privacy leakage to unauthorized parties, as well as demonstrating the utility and the efficiency of POCR using simulations. Refereed/Peer-reviewed

Searchable encryption is an important research area in cloud computing. However, most existing efficient and reliable ciphertext search schemes are based on keywords or shallow semantic parsing, which are not smart enough to meet with users’ search intention. Therefore, in this paper, we propose a content-aware search scheme, which can make semantic search more smart. First, we introduce conceptual graphs (CGs) as a knowledge representation tool. Then, we present our two schemes (PRSCG and PRSCG-TF) based on CGs according to different scenarios. In order to conduct numerical calculation, we transfer original CGs into their linear form with some modification and map them to numerical vectors. Second, we employ the technology of multi-keyword ranked search over encrypted cloud data as the basis against two threat models and raise PRSCG and PRSCG-TF to resolve the problem of privacy-preserving smart semantic search based on CGs. Finally, we choose a real-world data set: CNN data set to test our scheme. We also analyze the privacy and efficiency of proposed schemes in detail. The experiment results show that our proposed schemes are efficient.

In modern rail transport systems, balises are widely used to exchange track–train information via air-gap interface. In this paper, we first present the vulnerabilities on the standard balise air-gap interface, and then conduct vulnerability simulations using the system parameters that were specified in the European Train Control System. The simulation results show that the vulnerabilities can be exploited to launch effective and practical attacks, which could lead to catastrophic consequences, such as train derailment or collision. To mitigate the vulnerabilities and attacks, we propose to implement a challenge–response authentication process in the air-gap interface in the existing transport infrastructure.

Existing differential evolution (DE) algorithms often face two challenges. The first is that the optimization performance is significantly affected by the ad hoc configurations of operators and parameters for different problems. The second is the long runtime for real-world problems whose fitness evaluations are often expensive. Aiming at solving these two problems, this paper develops a novel double-layered heterogeneous DE algorithm and realizes it in cloud computing distributed environment. In the first layer, different populations with various parameters and/or operators run concurrently and adaptively migrate to deliver robust solutions by making the best use of performance differences among multiple populations. In the second layer, a set of cloud virtual machines run in parallel to evaluate fitness of corresponding populations, reducing computational costs as offered by cloud. Experimental results on a set of benchmark problems with different search requirements and a case study with expensive design evaluations have shown that the proposed algorithm offers generally improved performance and reduced computational time, compared with not only conventional and a number of state-of-the-art DE variants, but also a number of other distributed DE and high-performing evolutionary algorithms. The speedup is significant especially on expensive problems, offering high potential in a broad range of real-world applications.

In this paper, we propose a machine learning-based approach to detect malicious mobile malware in Android applications. This paper is able to capture instantaneous attacks that cannot be effectively detected in the past work. Based on the proposed approach, we implemented a malicious app detection tool, named Androidetect. First, we analyze the relationship between system functions, sensitive permissions, and sensitive application programming interfaces. The combination of system functions has been used to describe the application behaviors and construct eigenvectors. Subsequently, based on the eigenvectors, we compare the methodologies of naive Bayesian, J48 decision tree, and application functions decision algorithm regarding effective detection of malicious Android applications. Androidetect is then applied to test sample programs and real-world applications. The experimental results prove that Androidetect can better detect malicious applications of Android by using a combination of system functions compared with the previous work.