{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T20:08:39Z","timestamp":1778789319408,"version":"3.51.4"},"reference-count":0,"publisher":"Privacy Enhancing Technologies Symposium Advisory Board","issue":"2","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["PoPETs"],"abstract":"<jats:p>We describe a new paradigm for multi-party private set intersection cardinality (PSI-CA) 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. By operating under the assumption that a particular subset of parties refrains from collusion, 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 PSI-CA 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 PSI-CA 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.<\/jats:p>","DOI":"10.56553\/popets-2024-0041","type":"journal-article","created":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T20:05:27Z","timestamp":1712261127000},"page":"73-90","source":"Crossref","is-referenced-by-count":10,"title":["Multiparty Private Set Intersection Cardinality and Its Applications"],"prefix":"10.56553","volume":"2024","author":[{"given":"Jiahui","family":"Gao","sequence":"first","affiliation":[{"name":"Arizona State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ni","family":"Trieu","sequence":"additional","affiliation":[{"name":"Arizona State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Avishay","family":"Yanai","sequence":"additional","affiliation":[{"name":"VMware Research"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"35752","published-online":{"date-parts":[[2024,4]]},"container-title":["Proceedings on Privacy Enhancing Technologies"],"original-title":[],"deposited":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T20:27:07Z","timestamp":1719260827000},"score":1,"resource":{"primary":{"URL":"https:\/\/petsymposium.org\/popets\/2024\/popets-2024-0041.php"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["10.56553\/popets-2024-0041"],"URL":"https:\/\/doi.org\/10.56553\/popets-2024-0041","relation":{},"ISSN":["2299-0984"],"issn-type":[{"value":"2299-0984","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4]]}}}