Paper 2022/735
Multiparty Private Set Intersection Cardinality and Its Applications
Abstract
We describe a new paradigm for multi-party private set intersection cardinality (\psica) that allows $n$ 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 $n=16$ parties with data-sets of $2^{20}$ 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.
Metadata
- Available format(s)
-
PDF
- Publication info
- Preprint.
- Contact author(s)
-
jhgao @ asu edu
ntrieu1 @ asu edu
ay yanay @ gmail com - History
- 2023-09-10: revised
- 2022-06-08: received
- See all versions
- Short URL
- https://ia.cr/2022/735
- License
-
CC BY
BibTeX
@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}
}