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Efficient Privacy-Preserving User Tracking From Threshold Multi-Party Private Set Intersection
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AI
DOI：10.1109/tifs.2026.3705319.png)
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Abstract
En 中文
The ubiquitous sensing capabilities of the Internet of Things (IoT) enable large-scale user tracking by identifying users who appear in at least $t$ distributed location datasets. However, the distribution of these datasets across multiple tracking entities significantly increases the risk of sensitive data exposure. To address this problem, threshold multi-party private set intersection (T-MPSI) provides a promising privacy-preserving solution. Although the known works about T-MPSI have made valuable contributions, especially in terms of security, the efficiency deficiency in current T-MPSI protocols becomes apparent in large-scale deployment for user tracking. The core challenge is to develop an efficient T-MPSI protocol under the relaxed security constraint that is acceptable for user tracking. We first design a lightweight batch replicated secret sharing private membership test protocol with high performance. Moreover, we develop a one-round secure aggregation algorithm that bridges the gap between the secure query and the secure comparison built upon replicated secret sharing. Building on these techniques, we present an efficient T-MPSI protocol tailored to the designated $k$ -collusion model. Our protocol significantly enhances secure query efficiency and ensures that the communication complexity of secure comparison remains independent of the number of parties. We formally prove its security, and extensive experiments in a LAN setting demonstrate at least a $6\times $ speedup for secure query and a $3\times $ speedup for secure comparison over the state-of-the-art protocol. These results confirm the practicality and efficiency of the proposed protocol for privacy-preserving user tracking.
Keywords:
Data security
secure computation
threshold multi-party private set intersection
user tracking
Internet of Things
Journal
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8
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