Privately Computing Set-Union and Set-Intersection Cardinality via Bloom Filters

By Rolf Egert, Marc Fischlin, David Gens, Sven Jacob, Matthias Senker, Jörn Tillmanns. 2015

72 citations recorded by the source corpus.

How researchers see this paper

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Efficient Private Statistics with Succinct Sketches (2015, background): “However, their proposal is insecure, as shown by [53], who also introduces a novel Bloom filter based protocol for set-union and set-intersection cardinality.”
Differentially-Private Multi-Party Sketching for Large-Scale Statistics (2020, background): “Protocols based on MPC and Bloom lters have also been suggested [7, 27], but they did not consider dierential privacy of individual users.”
Efficient and Quasi-accurate Multiparty Private Set Union (2018, background): “One study [3] computed only the approximate number of unions.”
An Efficient Toolkit for Computing Private Set Operations (2017, methodology): “Dedicated techniques for computing these operations are given in [5,11,9] though designs are also given in the generic constructions of [18,1].”
Distributed Measurement with Private Set-Union Cardinality (2017, background): “Several protocols to securely compute set-union cardinality (or the related problem of set-intersection cardinality) have been proposed [12, 18, 32, 48].”
On Deploying Secure Computing Commercially: Private Intersection-Sum Protocols and their Business Applications (2019, methodology): “In this section, we describe a Private Intersection-Sum protocol based on the use of encrypted Bloom Filters, extending PSI approaches [16, 20, 19].”