Digital forensics is crucial to fight crimes around the world. Decentralized Digital Forensics (DDF) promotes it to another level by channeling the power of blockchain into digital investigations. In this work, we focus on the privacy and security of DDF. Our motivations arise from (1) how to track an anonymous- and-malicious data user who leaks only a part of the previously requested data, (2) how to achieve access control while protecting data from untrusted data centers, and (3) how to enable efficient and secure search on the blockchain. To address these issues, we propose Themis: an anonymous and secure DDF scheme with traceable anonymity, private access control, and efficient search. Our framework is boosted by establishing a Trusted Execution Environment in each authority (blockchain node) for securing the uploading, requesting, and searching. To instantiate the framework, we design a secure and robust watermarking scheme in conjunction with decentralized anonymous authentication, a private and fine-grained access control scheme, and an efficient and secure search scheme based on a dynamically updated data structure. We formally define and prove the privacy and security of Themis. We build a prototype with Ethereum and Intel SGX2 to evaluate its performance, which supports processing data from a considerable number of data providers and investigators.

@en

Range queries allow data users to outsource their data to a Cloud Server (CS) that responds to data users who submit a request with range conditions. However, security concerns hinder the wide-scale adoption. Existing works neglect item availability, fail to protect secure verification or sacrifice search accuracy for efficiency. In this paper, we propose Secure, Available, Verifiable, and Efficient (SAVE) range query processing, which has three distinctive features. (1) Secure availability checking against a malicious CS: we design a keyed index-based secure verification mechanism to check the availability of matched nodes, including validity and freshness. (2) Secure result verification: we design a targeted verification mechanism for result correctness and completeness while not compromising security. (3) Improved efficiency and accuracy: we design a lay-ered encoding method to improve search efficiency and accuracy. We formally stated and proved the security of SAVE in the random oracle model. We conducted extensive experiments over the Yelp and FourSquare dataset to validate the efficiency, e.g., a query over 10 thousand data items only needs 19.4 ms to get queried results and 3.5 ms for local verification.@en

Outsourcing data users' location data to a cloud server (CS) enables them to obtain kk nearest points of interest. However, data users' privacy concerns hinder the wide-scale use. Several studies have achieved Secure k Nearest Neighbor (SkNN) query, but do not address time-restricted access or result privacy, and randomly partition data items which degrades efficiency. In this article, we propose Time-restricted, verifiable, and efficient Query Processing (TiveQP). TiveQP has three distinguishing features. 1) Expand SkNN: data users can query kk nearest locations open at a specific time. 2) Adopt a stronger threat model: we assume the CS is malicious and propose complementary set (i.e., transform proving 'in' a set to proving 'in' its complementary set) to allow data users to verify results without leaking unqueried data items' information. 3) Improve efficiency: we design a space encoding technique and a pruning strategy to improve efficiency in query processing and result verification. We formally proved the security of TiveQP in the random oracle model. We conducted extensive evaluations over a Yelp dataset to show that TiveQP significantly improves over existing work, e.g., top-10NN query over 100 thousand data items only needs 10 ms to get queried results and 1.4 ms for verification.

@en

Vehicular Digital Forensics (VDF) is essential to enable liability cognizance of accidents and fight against crimes. Ensuring the authority to timely gather, analyze, and trace data promotes vehicular investigations. However, adversaries crave the identity of the data provider/user, damage the evidence, violate evidence jurisdiction, and leak evidence. Therefore, protecting privacy and evidence accountability while guaranteeing access control and traceability in VDF is no easy task. To address the above-mentioned issues, we propose Eunomia: an anonymous and secure VDF scheme based on blockchain. It preserves privacy with decentralized anonymous credentials without trusted third parties. Vehicular data and evidence are uploaded by data providers to the blockchain and stored in distributed data storage. Each investigation is modeled as a finite state machine with state transitions being executed by smart contracts. Eunomia achieves fine-grained evidence access control via ciphertext-policy attribute-based encryption and Bulletproofs. A user must hold specific attributes and a temporary-and unexpired token/warrant to retrieve data from the blockchain. Finally, a secret key is embedded into data to trace the traitor if any evidence breach happens. We use a formal analysis to demonstrate the strong privacy and security properties of Eunomia. Moreover, we build a prototype in a WiFi-based Ethereum test network to evaluate its performance.@en

Traffic monitoring services collect traffic reports and respond to users' traffic queries. However, the reports and queries may reveal the user's identity and location. Although different anonymization techniques have been applied to protect user privacy, a new security threat arises, namely, n-by-1 jamming attack, in which an anonymous contributing driver impersonates n drivers and uploads n normal reports by using n reporting devices. Such an attack will mislead the traffic monitoring service provider and further degrade the service quality. Existing traffic monitoring services do not support customized queries, and private information retrieval techniques cannot be applied directly in traffic monitoring. We formally define the new attack and propose a traffic monitoring scheme TraJ to defend the attack and achieve user-defined location privacy. Specifically, we bridge anonymous contributing drivers without disclosing their speed set by using private set intersection. Each RSU collects time traffic reports and structures a weighted proximity graph to filter out malicious colluding drivers. We design a user-defined privacy-preserving query method by encoding complex road network. We leverage the uploading phase from private aggregation to collect traffic conditions and allow requesting drivers to dynamically and privately query traffic conditions. We provide a formal analysis of TraJ to prove its privacy and security properties. We also construct a prototype based on a real-world dataset and Android smartphones to demonstrate its feasibility and efficiency. A formal analysis demonstrates the privacy and security properties. Extensive experiments illustrate the performance and defense efficacy.

@en

Current connected and autonomous vehicles will contribute to various and green vehicular services. However, sharing personal data with untrustworthy Navigation Service Providers (NSPs) raises serious location concerns. To address this issue, many Location Privacy-Preserving Mechanisms (LPPMs) have been proposed. In addition, several quantification methods have been designed to help understand location privacy and illustrate how location privacy is leaked. However, their assessment is insufficient due to the incomplete assumptions about the adversary’s model. In particular, users tend to request the same navigation routes from home to workplace and acquire traffic information along the route. An adversary can collect the coordinates of adjacent locations and infer the two true locations. In this paper, we provide a formal framework for the analysis of LPPMs in navigation services. Our framework captures extra information that is available to an adversary performing localization attacks. By formalizing the adversary’s performance, we also propose and justify two new metrics to quantify location privacy in navigation services, namely accuracy and visibility. We assess the efficacy of two popular LPPMs for location privacy, i.e., differential privacy and k-anonymity. Experimental results demonstrate that the adversary can recover users’ locations with a high probability.@en

E-commerce platforms incorporate reputation systems that allow customers to rate suppliers following financial transactions. Existing reputation systems cannot defend the centralized server against arbitrarily tampering with the supplier’s reputation. Furthermore, they do not offer reputation access across platforms. Rates are faced with privacy leakages because rating activities are correlated with privacy (e.g., identity and rating). Meanwhile, raters could be malicious and initiate multiple rating attacks and abnormal rating attacks. Determining how to address these issues have both research and practical value. In this paper, we propose a blockchain-based privacy-preserving reputation system for e-commerce platforms named RepChain; our system allows cross-platform reputation access and anonymous and private ratings. Using RepChain, all e-commerce platforms collaborate and share users’ reputations by co-constructing a consortium blockchain and modeling the rating process as a finite state machine. In particular, we facilitate one-show anonymous credentials constructed from two-move blind signatures to protect customers’ identities and resist multiple rating attacks, leverage zero-knowledge range proof to verify the correctness of ratings and defend against abnormal rating attacks, design a secure sum computation protocol among nodes to update reputations, and verify ratings via batch processing and consensus hashes. Finally, we demonstrate the security and privacy of RepChain via a formal analysis and evaluate its performance based on Ethereum test network.@en

Vehicular networks allow billions of vehicular users to be connected to report and exchange real-time data for offering various services, such as navigation, ride-hailing, smart parking, traffic monitoring, and vehicular digital forensics. Fifth generation (5G) is a new radio access technology with greater coverage, accessibility, and higher network density. 5G-supported Vehicular Networks (5GVNs) have attracted plenty of attention from both academia and industry. Geared with new features, they are expected to revolutionize the mobility ecosystem to empower a portfolio of new services. Meanwhile, the development of such communication capabilities, along with the development of sensory devices and the enhancement of local computing powers, have lead to an inevitable reality of massive data (e.g., identity, location, and trajectory) collection from vehicular users. Unfortunately, 5GVN are still confronted with a variety of privacy threats. Such threats are targeted at users’ data, identity, location, and trajectory. If not properly handled, such threats will cause unimaginable consequences to users. In this survey, we first review the state-of-the-art of survey papers. Next, we introduce the architecture, features, and services of 5GVN, followed by the privacy objectives of 5GVN and privacy threats to 5GVN. Further, we present existing privacy-preserving solutions and analyze them in-depth. Finally, we define some future research directions to draw more attention and down-to-earth efforts into this new architecture and its privacy issues.@en

Ride-Hailing Service (RHS) has motivated the rise of innovative transportation services. It enables riders to hail a cab or private vehicle at the roadside by sending a ride request to the Ride-Hailing Service Provider (RHSP). Such a request collects rider’s real-time locations, which incur serious privacy concerns for riders. While there are many location privacy-preserving mechanisms in the literature, few of them consider mobility patterns or location semantics in RHS. In this work, we propose a pick-up location recommendation scheme with location indistinguishability and semantic indistinguishability for RHS. Specifically, we give formal definitions of location indistinguishability and semantic indistinguishability. We model the rider mobility as a time-dependent first-order Markov chain and generates a rider’s mobility profile. Next, it calculates the geographic similarity between riders by using the Mallows distance and classifies them into different geographic groups. To comprehend the semantics of a location, it extracts such information through user-generated content from two popular social networks and obtains the semantic representations of locations. Cosine similarity and unified hypergraph are used to compute the semantic similarities between locations. Finally, it outputs a set of recommended pick-up locations. To evaluate the performance, we build our mobility model over the real-world dataset GeoLife, analyze the computational costs of a rider, show the utility, and implement it on an Android smartphone. The experimental results show that it costs less than 0.12 ms to recommend 10 pick-up locations within 500 m of walking distance.@en

Location-based services (LBSs) are widely deployed in commercial services. These services always depend on a service provider, e.g., a cloud server, to store the enormous amounts of geospatial data and to process various queries. For example, a Yelp user can retrieve a list of recommended cafés by submitting her/his current location to the service provider. While LBSs offer tremendous benefits, it is vital to safeguard users’ privacy against untrusted service providers. However, no prior secure k nearest neighbor query processing schemes satisfy the three security requirements of one-time, oblivious, and unlinkable. In particular, we are concerned with the problem of item exclusion: how to match one data query with each item on the cloud no more than once in an oblivious and unlinkable manner. In this paper, we propose the first secure k nearest neighbor query processing scheme, Obaq, that satisfies the above requirements. Obaq first introduces an item identifier into an existing secure k nearest neighbor query processing scheme. Each data owner inserts an item identifier and her/his location information into a secure index, and each data user transfers the identifier of a previously received data item and location information into a specific range. Then, Obaq excludes corresponding items via privacy-preserving range querying. We define strong index privacy and strong token privacy and formally prove the security of Obaq in the random oracle model. We further evaluate the performance of Obaq using a prototype and a real-world dataset. The experimental results show that Obaq is highly efficient and practical in terms of computational cost, communication overhead, and response delay.

@en

Energy trading in Industrial Internet of Things (IIoT), a fundamental approach to realize Industry 4.0, plays a vital role in satisfying energy demands and optimizing system efficiency. Existing research works utilize a utility company to distribute energy to energy nodes with the help of energy brokers. Afterwards, they apply blockchain to provide transparency, immutability, and auditability of peer-to-peer (P2P) energy trading. However, their schemes are constructed on a weak security model and do not consider the cheating attack initiated by energy sellers. Such an attack refers to an energy seller refusing to transfer the negotiated energy to an energy purchaser who already paid money. In this article, we propose FeneChain, a blockchain-based energy trading scheme to supervise and manage the energy trading process toward building a secure energy trading system and improving energy quality for Industry 4.0. Specifically, we leverage anonymous authentication to protect user privacy, and we design a timed-commitments-based mechanism to guarantee the verifiable fairness during energy trading. Moreover, we utilize fine-grained access control for energy trading services. We also build a consortium blockchain among energy brokers to verify and record energy trading transactions. Finally, we formally analyze the security and privacy of FeneChain and evaluate its performance (i.e., computational costs and communication overhead) by implementing a prototype via a local Ethereum test network and Raspberry Pi.

@en