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Private Set Operations from Oblivious Switching

Authors:
Gayathri Garimella
Payman Mohassel
Mike Rosulek
Saeed Sadeghian
Jaspal Singh
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DOI: 10.1007/978-3-030-75248-4_21
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Abstract: Private set intersection reveals the intersection of two private sets, but many real-world applications require the parties to learn $\textit{only}$ partial information} about the intersection. In this paper, we introduce a new approach for computing arbitrary functions of the intersection, provided that it is safe to also reveal the cardinality of the intersection. In the most general case, our new protocol provides the participants with secret shares of the intersection, which can be fed into any generic 2PC protocol. Certain computations on the intersection can also be done even more directly and efficiently, avoiding this secret-sharing step. These cases include computing $\textit{only}$ the cardinality of the intersection, or the ``cardinality-sum'' application proposed in Ion $\textit{et al.}$ (ePrint 2017). Compared to the state-of-the-art protocol for computing on the intersection (Pinkas et al., Eurocrypt 2019), our protocol has about $2.5-3\times$ less communication and has faster running time on slower (50Mbps) networks. Our new techniques can also be used to privately compute the {\em union} of two sets as easily as computing the intersection. Our protocol concretely improves the leading private set union protocol (Kolesnikov et al., Asiacrypt 2020) by a factor of $2-2.5\times$, depending on the network speed. We then show how private set union can be used in a simple way to realize the ``Private-ID'' functionality suggested by Buddhavarapu et al.~(ePrint 2020). Our protocol is significantly faster than the prior Private-ID protocol, especially on fast networks. All of our protocols are in the two-party setting and are secure against semi-honest adversaries.
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BibTeX
@article{pkc-2021-30999,
  title={Private Set Operations from Oblivious Switching},
  booktitle={Public-Key Cryptography - PKC 2021},
  publisher={Springer},
  doi={10.1007/978-3-030-75248-4_21},
  author={Gayathri Garimella and Payman Mohassel and Mike Rosulek and Saeed Sadeghian and Jaspal Singh},
  year=2021
}