Private Set Intersection (PSI) is a significant application of interest within Secure Multi-party Computation (MPC), even though we are still in the early stages of deploying MPC solutions to real-world problems. Threshold PSI (tPSI), a variant of PSI, allows two parties to determine the intersection of their respective sets only if the cardinality of the intersection is at least (or less than) a specified threshold t. In this paper, we propose a generic construction for two-party tPSI that extensively utilizes Oblivious Transfer (OT). Our approach is based on lightweight primitives and avoids costly public-key systems such as homomorphic encryption. We start by introducing the secret-sharing private membership test ${}^{\mathrm{ss}}\mathrm{PMT}$ that is based on the secret-sharing private equality test ${}^{\mathrm{ss}}\mathrm{PEQT}$. The ${}^{\mathrm{ss}}\mathrm{PMT}$ enables tPSI to be scaled for a wide range of practical applications, particularly benefiting parties with limited computational resources. Consequently, two distinct two-party tPSI protocols can be efficiently implemented: over-threshold PSI ($t^{\le }\mathrm{PSI}$) and under-threshold PSI $t^{>}\mathrm{PSI}$. In addition, we propose a lightweight two-party tPSI with limited leakage and a generic precomputing OT suitable for phased implementation. Experimental performance demonstrates that our protocols are highly efficient and computationally friendly, thus paving the way for broader deployment of tPSI solutions.

Ride-sharing services provide automated route matching for drivers and passengers with similar itineraries and schedules, which alleviate traffic congestion and reduce travel costs. To protect the location privacy in the ride-sharing process, a variety of privacy-preserving matching schemes were proposed. However, the existing matching schemes incur high computational overhead, and cannot resist the attack of malicious adversaries. In this paper, we proposed a privacy-preserving matching scheme based on threshold private set intersection, which can protect the location privacy in the malicious model. To reduce computational overhead, we designed a new efficient method for the cardinality test of the route intersection size, based on symmetric-key primitives and a constant number of public-key operations. In addition, our privacy-preserving matching scheme uses geographic indistinguishability to generate virtual points as input sets of privacy-preserving matching scheme, and the Internet of Vehicles (IoV) uses the output of privacy-preserving matching scheme to match the appropriate vehicle for the user. The performance evaluation shows that the proposed scheme is efficient for IoV-enabled ride-sharing services.

Keyword Private Information Retrieval (Keyword PIR) enables private querying over keyword-based databases, which are typically sparse, as opposed to the dense arrays used in standard Index PIR. However, existing Keyword PIR schemes are limited to single-keyword queries and generally assume that keywords serve as unique identifiers, making them inadequate for practical scenarios where keywords are non-unique attributes and clients need to retrieve records matching multiple keywords simultaneously. To bridge this gap, we propose MkCwPIR, the first single-round, exact-match multi-keyword PIR protocol that supports conjunctive keyword queries while preserving strict keyword privacy against the server. Our construction employs Constant-weight codes and Newton–Girard identities to encode multi-keyword selection into a compact algebraic representation, representing a functional extension of CwPIR (Usenix Security ’22). While this functional expansion introduces additional computational overhead due to the processing of multiple keywords, we further introduce VMkCwPIR—an optimized variant leveraging BFV vectorized homomorphic encryption. Experimental results demonstrate that although the base MkCwPIR incurs higher latency due to its enhanced logical capabilities, the vectorized optimizations in VMkCwPIR effectively close this performance gap. Consequently, VMkCwPIR achieves a performance level comparable to the single-keyword CwPIR. Experimental results demonstrate that when processing a query with eight keywords, VMkCwPIR achieves a server-side execution time comparable to executing only four independent single-keyword queries in CwPIR, while maintaining constant communication overhead for up to 16 keywords.