Efficient and Privacy-Preserving Non-Interactive Ride Matching over Encrypted Multi-Attribute Data in Mobile Crowdsourcing

Abstract

Ride matching has become a representative application in mobile crowdsourcing, where passengers’ travel demands are matched with available driver resources. However, most existing schemes assume that drivers remain continuously online to support interactive key exchange, which is impractical in offline ride matching scenarios. Moreover, ride-hailing platforms are typically untrusted and may attempt to infer users’ sensitive information. Existing studies also mainly focus on spatial and interest attributes, while overlooking drivers’ available service time. To address these issues, we propose an Efficient and Privacy-Preserving Ride Matching scheme over encrypted Multi-Attribute Data (EPRMM) for mobile crowdsourcing. By integrating secure inner product computation with a non-interactive key agreement mechanism, EPRMM realizes unified matrix-based encryption and matching over spatial location, temporal constraints, and interest keywords in an untrusted server environment. In addition, a deterministic matching mechanism is introduced to transform conventional ranking-based matching into the efficient evaluation of two inner products, thereby reducing computational cost while preserving matching accuracy and supporting non-interactive driver participation. Formal security analysis shows that EPRMM guarantees data confidentiality and query privacy under the IND-CPA security model. Experimental results demonstrate that EPRMM outperforms existing non-interactive multi-attribute encrypted query schemes in both query efficiency and overall overhead. In particular, it reduces ride matching time by approximately 3.5 times and the overall matching overhead by about 2 times, indicating its practical applicability in large-scale ride matching scenarios.

Keywords

Ride Matching / Crowdsourcing / Non-Interactive / Privacy Protection

RIGHTS & PERMISSIONS

Higher Education Press 2026

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