Jiachen Shen

The Internet of Medical Things (IoMT) consists of many resource-constrained medical devices that provide patients with medical services anytime and anywhere. In such an environment, the collection and sharing of medical records raise serious security concerns. Although various cryptographic schemes have been proposed, most fail to address two critical threats simultaneously: (i) sensitive data exposure caused by external cloud servers and/or open network environments; (ii) algorithm substitution attacks (ASAs) by internal adversaries. Furthermore, when data owners (e.g., delegators) are inconvenient to process their data, it is desirable to establish a more fine-grained way to delegate encryption rights. To tackle these issues, we propose a subversion-resistant autonomous path proxy re-encryption with an equality test function (SRAP-PRET). Specifically, our scheme allows the delegator to create a multi-hop delegation path in advance with the delegator's preferences, effectively preventing unauthorized access. By deploying a cryptographic reverse firewall zone, SRAP-PRET addresses the problem of information leakage caused by adversaries initiating ASAs. Additionally, SRAP-PRET also supports secure deduplication and efficient data decryption. Security analysis shows that SRAP-PRET provides resistance against ASAs and security against chosen plaintext attacks. Performance evaluations demonstrate that SRAP-PRET offers enhanced security properties without sacrificing efficiency.

Zero-knowledge Succinct Non-interactive Argument of Knowledge (zkSNARK) is a powerful cryptographic primitive that enables a prover to convince a verifier that something is true without leaking the private witness. Current zkSNARKs face significant computational costs in generating proofs, which restricts their use in areas like private payments, confidential smart contracts, and anonymous credentials. Private delegation offers a practical solution by outsourcing the heavy computation to powerful external workers without leaking any private information. In this work, we propose HyperSiniel, an efficient private delegation framework for general zkSNARKs that achieves a new feature called guaranteed output delivery (GOD). HyperSiniel is designed to be compatible with any universal zkSNARKs constructed from a polynomial interactive oracle proof (PIOP) and a polynomial commitment scheme (PCS). It enables a computationally limited delegator to outsource proof generation to several workers in a fully non-interactive and privacy-preserving manner. Compared to the most state-of-the-art frameworks (e.g., Siniel [NDSS'25]), HyperSiniel ensures that the delegator always receives a correct proof, regardless of malicious worker behavior. We implement HyperSiniel and compare the performance with Siniel across varying bandwidths and circuit sizes. Under low-bandwidth conditions (10MBps), HyperSiniel incurs only an additional 25% overhead compared with Siniel, while the total running time of HyperSiniel is almost identical to Siniel under high-bandwidth settings (1000MBps). These results show that the strong robustness guarantee of GOD in HyperSiniel comes almost for free, making it a practical and secure solution for real-world zkSNARK delegation.

The Internet of Things (IoT) deployments generate continuous streams of device logs and sensor readings that are offloaded to the cloud. Devices are resource-constrained and clouds are only partially trusted, so data are encrypted before upload. Operators must still search these ciphertexts—and queries are often approximate. Searchable encryption (SE) allows users to securely outsource their data to an untrusted cloud server (CS) without revealing sensitive information. However, most existing SE protocols only support exact single-keyword search, which significantly limits their practical applicability. Moreover, most SE protocols inevitably leak access or search patterns to the server. Recent studies show that such leakage can be exploited to infer sensitive information about the data user (DU). To address these issues, this article introduces PolySE, a fuzzy SE (FSE) scheme that allows users to securely perform fuzzy search over encrypted data. PolySE uses locality-sensitive hashing (LSH, AccuracyLSH), simple hashing, and polynomial encoding in the preprocessing phase to ensure accuracy and efficiency. Afterward, the DU and the CS run a secure oblivious polynomial evaluation (OPE) protocol to obtain the search result. The security analysis shows that PolySE is secure against semihonest adversaries and minimizes information leakage to the CS. Overall, PolySE targets the IoT data pipelines: it supports accurate fuzzy queries over encrypted device logs with low client-side cost, constant-size per-query communication, and practical pattern hiding, making it deployable on resource-constrained gateways. Finally, we compare PolySE with the state-of-the-art schemes to demonstrate its improvements in query accuracy, search time, and storage overhead. For a dataset with 35 000 keyword–document pairs, PolySE improves query accuracy by 6.5% and 12.5%, reduces query time by 85% and 86%, and decreases storage overhead by 86% and 80% compared to prime inner product encodings (PIPEs) and efficient leakage-resilient multi-keyword fuzzy search (EliMFS-E), respectively.

With the rapid proliferation of Internet of Things (IoT)-enabled healthcare systems, sensitive data, such as electronic health records (EHRs) are increasingly outsourced to semi-trusted e-health clouds, raising critical concerns about privacy and verifiability. This article investigates secure and efficient keyword-based data retrieval in such environments. key-aggregate searchable encryption (KASE) enables a data owner to share multiple encrypted documents with users using a single compact key, but existing schemes offer limited resistance to offline keyword guessing attacks (KGA) and insufficient protection against keyword privacy leakage. To systematically expose broader vulnerabilities in existing KASE constructions, we present a novel and general attack, termed expanded inside trapdoor attack (EITA), which targets both the security of aggregate keys and the privacy of keywords in unauthorized documents. Furthermore, flexible search methods and result verification are essential for improving KASE performance. To this end, we propose a new KASE scheme that supports conjunctive queries and employs aggregate signatures for verification, while being able to resist KGA and EITA. The security analysis and performance evaluation both demonstrate that our proposed scheme is provably secure and practically efficient in IoT-enabled healthcare settings.

Secure task matching has been a crucial research problem in crowdsourcing, requiring the alignment of workers’ preferences and requesters’ task requirements while ensuring user privacy and matching integrity. Recently, some researchers applied blockchain to crowdsourcing, either replacing the platform for decentralization or recording proofs for public verification to defend against malicious platforms. However, they still suffer from unitary coarse-grained matching models or expensive on-chain costs. To address these limitations, we propose PGVMatch, a privacy-aware and fine-grained crowdsourcing task-matching scheme with lightweight on-chain public verifiability. Our scheme is constructed on our newly proposed cryptographic primitive–Multi-authority Attribute-Based Keyword Search with Public Verifiability (MABKS-PV), which avoids access policy leakage and key escrow risks on a single authority, meanwhile adding constant-size proof generation and lightweight verification algorithms to a basic ABKS construction. In PGVMatch, requesters can select workers with fine-grained attribute demands, and workers can pick interested tasks with multi-keyword search, preserving dual-side privacy. The matching process is conducted off-chain, while constant-size proofs are recorded on-chain for efficient and public verification of matching integrity. Security analysis and extensive experiments on the Hyperledger Fabric blockchain demonstrate both the security and our superior performance. PGVMatch outperforms the existing scheme with the fastest matching result verification, achieving a 29% improvement in throughput and a 33% reduction in latency.

This paper proposes a symmetric searchable encryption scheme that hides both search pattern and access pattern. The protocol allows users to obtain correct search results on ciphertexts without leaking privacy. We introduce an efficient and secure Oblivious polynomial evaluation protocol (ESOPE) to achieve pattern hiding. Compared to existing schemes, our approach eliminates the need for auxiliary participants, thereby reducing the risk of privacy breaches. Moreover, our protocol demonstrates significant improvements in both time efficiency and storage efficiency across datasets of various sizes.

This paper introduces a novel multi-party private set intersection (MPSI) protocol, enabling multiple parties to collaboratively compute the intersection of their input sets while ensuring no private information is disclosed. Our protocol mainly leverages efficient oblivious key-value store (OKVS) technique and symmetric-key operations, requiring only 3 rounds of interaction. We implement our protocol using various set sizes and numbers of parties, and the experimental results demonstrate that it surpasses the performance of existing state-of-the-art protocols.