Fast Private Set Intersection from Homomorphic Encryption
Hao Chen, Kim Laine, Peter Rindal
摘要
Private Set Intersection (PSI) is a cryptographic technique that allows two parties to compute the intersection of their sets without revealing anything except the intersection. We use fully homomorphic encryption to construct a fast PSI protocol with a small communication overhead that works particularly well when one of the two sets is much smaller than the other, and is secure against semi-honest adversaries. The most computationally efficient PSI protocols have been constructed using tools such as hash functions and oblivious transfer, but a potential limitation with these approaches is the communication complexity, which scales linearly with the size of the larger set. This is of particular concern when performing PSI between a constrained device (cellphone) holding a small set, and a large service provider (e.g. WhatsApp), such as in the Private Contact Discovery application. Our protocol has communication complexity linear in the size of the smaller set, and logarithmic in the larger set. More precisely, if the set sizes are N y < N x , we achieve a communication overhead of O(N y log N x ). Our running-time-optimized benchmarks show that it takes 36 seconds of online-computation, 71 seconds of non-interactive (receiver-independent) pre-processing, and only 12.5MB of round trip communication to intersect five thousand 32-bit strings with 16 million 32-bit strings. Compared to prior works, this is roughly a 38-115× reduction in communication with minimal difference in computational overhead.
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- PIR with Compressed Queries and Amortized Query ProcessingSebastian Angel, Hao Chen, Kim Laine, Srinath T. V. SettyS&P 2018 · 被引用 353 次
- Privacy Preserving Vertical Federated Learning for Tree-based ModelsYuncheng Wu, Shaofeng Cai, Xiaokui Xiao, Gang Chen 等VLDB 2020 · 被引用 259 次
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- Labeled PSI from Fully Homomorphic Encryption with Malicious SecurityHao Chen, Zhicong Huang, Kim Laine, Peter RindalCCS 2018 · 被引用 242 次
- Feature Inference Attack on Model Predictions in Vertical Federated LearningXinjian Luo, Yuncheng Wu, Xiaokui Xiao, Beng Chin OoiICDE 2021 · 被引用 212 次
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