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Practical Multi-Party Private Set Intersection Protocols
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DOI：10.1109/TIFS.2021.3118879.png)
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Abstract
En 中文
Privacy-preserving techniques for processing sets of information have attracted the research community's attention in recent years due to society's increasing dependency on the availability of data at any time. One of the fundamental problems in set operations is known as Private Set Intersection (PSI). The problem requires two parties to compute the intersection between their sets while preserving correctness and privacy. Although several efficient two-party PSI protocols already exist, protocols for PSI in the multi-party setting (MPSI) currently scale poorly with a growing number of parties, even though this applies to many real-life scenarios. This paper fills this gap by proposing two multi-party protocols based on Bloom filters and threshold homomorphic PKEs, which are secure in the semi-honest model. The first protocol is a multi-party PSI, whereas the second provides a more subtle functionality -threshold multi-party PSI (T-MPSI) - which outputs items of the server that appear in at least some number of other private sets. The protocols are inspired by the Davidson-Cid protocol based on Bloom filters. We compare our MPSI protocol against Kolesnikov et al., which is among the fastest known MPSI protocols. Our MPSI protocol performs better than Kolesnikov et al. in terms of run time, given that the sets are small and there is a large number of parties. Our T-MPSI protocol performs better than other existing works: the computational and communication complexities are linear in the number of elements in the largest set given a fixed number of colluding parties. We conclude that our MPSI and T-MPSI protocols are practical solutions suitable for emerging use-case scenarios with many parties, where previous solutions did not scale well.
Keywords:
Protocols
Roads
Privacy
Diseases
Complexity theory
Servers
Safety
Privacy-preserving protocols
PSI
MPSI
threshold MPSI
threshold PKE
Bloom filters
Journal
IF:
8
Papers:
5.3K
Citations:
2.3W
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