Threshold signature is a powerful cryptographic technique with a large number of real-life applications. As designed by Boneh and Komlo (CRYPTO’22), TAPS is a new threshold signature integrating privacy and accountability. It allows a combiner to combine t signature shares while protecting t and the signing group from the public. It also enables a tracer to trace a threshold signature to its original signing group. Despite being valuable, TAPS neglects the witnessing of tracing, i.e., leaves the tracing activity unrestrained. In this paper, we introduce Accountable and Private Threshold Signature with Hidden Witnesses (HiTAPS) that not only provides privacy and accountability, but also incorporates witnessed tracing. In specific, we first utilize Dynamic Threshold Public-Key Encryption (DTPKE) and ElGamal encryption to designate a set of t^′ witnesses for endorsing the tracing activity. We then compute a keyed-hash tag for the t^′ witnesses to initiate the tracing activity secretly. Moreover, we present an optimized protocol HiTAPS2 to reduce communication overhead of the combiner. We formalize the definitions, security, and privacy for HiTAPS. We formally prove its security and privacy. To evaluate the performance of HiTAPS and HiTAPS2, we build a prototype based on pypbc. Experimental results show that HiTAPS takes 217(370) ms to combine (track) a threshold signature of 5 signers (witnesses). The optimized HiTAPS2 only takes 137 ms to combine a threshold signature of 5 signers.

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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.

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Smart Parking Services (SPSs) enable cruising drivers to find the nearest parking lot with available spots, reducing the traveling time, gas, and traffic congestion. However, drivers risk the exposure of sensitive location data during parking query to an untrusted Smart Parking Service Provider (SPSP). Our motivation arises from a repetitive query to an updated database, i.e., how a driver can be repetitively paired with a previously-matched-but-forgotten lot. Meanwhile, we aim to achieve repetitive query in an oblivious and unlinkable manner. In this work, we present Mnemosyne2 : decentralized and privacy-preserving smart parking with secure repetition and full verifiability. Specifically, we design repetitive, oblivious, and unlinkable Secure k Nearest Neighbor (SkNN) with basic verifiability (correctness and completeness) for encrypted-andupdated databases. We build a local Ethereum blockchain to perform driver-lot matching via smart contracts. To adapt to the lot count update, we resort to the immutable blockchain for advanced verifiability (truthfulness). Last, we utilize decentralized blacklistable anonymous credentials to guarantee identity privacy. Finally, we formally define and prove privacy and security. We conduct extensive experiments over a real-world dataset and compare Mnemosyne2 with existing work. The results show that a query only needs 8 seconds (175 ms) on average for service waiting (verification) among 500 drivers.

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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.

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Ride-Hailing Service (RHS) has drawn plenty of attention as it provides transportation convenience for riders and financial incentives for drivers. Despite these benefits, riders risk the exposure of sensitive location data during ride requesting to an untrusted Ride-Hailing Service Provider (RHSP). Our motivation arises from repetitive matching, i.e., the same driver is repetitively assigned to the same rider. Meanwhile, we introduce a driver exclusion function to protect riders' location privacy. Existing work on privacy-preserving RHS overlooks this function. While Secure k Nearest Neighbor (SkNN) facilitates efficient matching, the state-of-the-art neglects a collusion attack. To solve this problem, we formally define repetitive matching and strong location privacy, and propose Mnemosyne: privacy-preserving ride matching with collusion-resistant driver exclusion. We extend the simple integration of equality checking and item exclusion to a dynamic integration. We concatenate each prefix of an acceptable identity range to each location code when generating a ride request, i.e., secure mix index. We process each prefix of the driver identity to generate a ride response, i.e., a mix token. We build an indistinguishable Bloom-filter as an index to query the token. When matching riders with drivers, the colluding parties cannot distinguish identity prefixes from location codes. We build a prototype of Mnemosyne based on servers, smartphones, and a real-world dataset. Experimental results demonstrate that Mnemosyne outperforms existing work regarding strong location privacy and computational costs.

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Shortest distance queries over large-scale graphs bring great benefits to various applications, i.e., save planning time and travelling expenses. To protect the sensitive nodes and edges in the graph, a user outsources an encrypted graph to an untrusted server without losing the query ability. However, no prior work has considered the user requirement of the shortest path with k unsorted nodes. In particular, we are concerned with how to securely find the shortest path by passing k nodes that do not have a fixed traverse order. To solve the problems, we propose Gespun (stands for Graph encryption for shortest path queries with k unordered nodes). It includes an oracle encryption scheme that is provably secure against the semi-honest server. Specifically, we compute the shortest paths and distances for all nodes locally to obtain path-distance oracles. We transform the shortest paths to a sequence of secure codes by using a pseudo-random permutation to protect the structure privacy. We encrypt the shortest distance by using additively homomorphic encryption. Second, we pack the oracles in link-list nodes and store them in an array-based dictionary after another permutation. Next, we construct a search graph to compute the shortest path while guaranteeing that the path passes the required k nodes. We formally prove that Gespun is adaptively semantically-secure in the random oracle. We implement a prototype of Gespun and evaluate its performance. Experiments results demonstrate that Gespun is efficient, e.g., a query over 6301 nodes, 20777 edges, and 5 unsorted nodes only needs 483 ms to get queried results. We believe that our research problem span new research that soon promotes a new line of graph encryption schemes.

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