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PSI-Stats: Private Set Intersection Protocols Supporting Secure Statistical Functions

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Applied Cryptography and Network Security (ACNS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13269))

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Abstract

Private Set Intersection (PSI) enables two parties, each holding a private set to securely compute their intersection without revealing other information. This paper considers settings of secure statistical computations over PSI, where both parties hold sets containing identifiers with one of the parties having an additional positive integer value associated with each of the identifiers in her set. The main objective is to securely compute some desired statistics of the associated values for which its corresponding identifiers occur in the intersection of the two sets. This is achieved without revealing the identifiers of the set intersection. In this paper, we present protocols which enable the secure computations of statistical functions over PSI, which we collectively termed PSI-Stats. Implementations of our constructions are also carried out based on simulated datasets as well as on actual datasets in the business use cases that we defined, in order to demonstrate practicality of our solution. PSI-Stats incurs \(5\times \) less monetary cost compared to the current state-of-the-art circuit-based PSI approach due to Pinkas et al. (EUROCRYPT’19). Our solution is more tailored towards business applications where monetary cost is the primary consideration.

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Notes

  1. 1.

    Technically only \(\mathcal {M'}\), which is a close approximation to \(\mathcal {M}\) can be computed by \( {B}\). In essence, this distinction is largely irrelevant in this specific context as we evaluate a stronger security setting than required where \( {B}\) has the knowledge of both \(\mathcal {M}\) and \(\mathcal {M'}\).

  2. 2.

    https://aws.amazon.com/ec2/spot/pricing. https://aws.amazon.com/cloudfront/pricing/.

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Acknowledgements

We thank Sherman Chow and the anonymous reviewers for their helpful comments, as well as Benny Pinkas, Thomas Schneider, Oleksandr Tkachenko and Avishay Yanai for initially providing us with their codes of the implementation in [34]. This work was supported by the NUS-NCS Joint Laboratory for Cyber Security, Singapore.

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Authors and Affiliations

  1. Seagate Technology, Singapore, Singapore

    Jason H. M. Ying

  2. NUS-Singtel Cyber Security R&D Laboratory, Singapore, Singapore

    Jason H. M. Ying, Shuwei Cao, Geong Sen Poh, Jia Xu & Hoon Wei Lim

  3. Trustwave, Singapore, Singapore

    Shuwei Cao, Geong Sen Poh, Jia Xu & Hoon Wei Lim

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  1. Jason H. M. Ying
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Correspondence to Jason H. M. Ying .

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  1. Stevens Institute of Technology, Hoboken, NJ, USA

    Giuseppe Ateniese

  2. Sapienza University of Rome, Rome, Italy

    Daniele Venturi

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Ying, J.H.M., Cao, S., Poh, G.S., Xu, J., Lim, H.W. (2022). PSI-Stats: Private Set Intersection Protocols Supporting Secure Statistical Functions. In: Ateniese, G., Venturi, D. (eds) Applied Cryptography and Network Security. ACNS 2022. Lecture Notes in Computer Science, vol 13269. Springer, Cham. https://doi.org/10.1007/978-3-031-09234-3_29

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  • DOI: https://doi.org/10.1007/978-3-031-09234-3_29

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