Multiparty Private Set Intersection Cardinality and Its Applications
Jiahui Gao, Arizona State University
Ni Trieu, Arizona State University
Avishay Yanai, VMware Research
Abstract
We describe a new paradigm for multi-party private set intersection cardinality (\psica) that allows parties to compute the intersection size of their datasets without revealing any additional information. We explore a variety of instantiations of this paradigm. Our protocols avoid computationally expensive public-key operations and are secure in the presence of a semi-honest adversary.
We demonstrate the practicality of our \psica\ with an implementation. For parties with data-sets of items each, our server-aided variant takes 71 seconds. Interestingly, in the server-less setting, the same task takes only 7 seconds. To the best of our knowledge, this is the first `special purpose' implementation of a multi-party \psica\ from symmetric-key techniques (i.e., an implementation that does not rely on a generic underlying MPC).
We study two interesting applications -- heatmap computation and associated rule learning (ARL) -- that can be computed securely using a dot-product as a building block. We analyse the performance of securely computing heatmap and ARL using our protocol and compare that to the state-of-the-art.
@misc{cryptoeprint:2022/735,
author = {Jiahui Gao and Ni Trieu and Avishay Yanai},
title = {Multiparty Private Set Intersection Cardinality and Its Applications},
howpublished = {Cryptology {ePrint} Archive, Paper 2022/735},
year = {2022},
url = {https://eprint.iacr.org/2022/735}
}
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