Practical multi-party private set intersection from symmetric-key techniques
Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications …, 2017•dl.acm.org
We present a new paradigm for multi-party private set intersection (PSI) that allows n parties
to compute the intersection 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 any number of semi-
honest participants (ie, without an honest majority). We demonstrate the practicality of our
protocols with an implementation. To the best of our knowledge, this is the first …
to compute the intersection 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 any number of semi-
honest participants (ie, without an honest majority). We demonstrate the practicality of our
protocols with an implementation. To the best of our knowledge, this is the first …
We present a new paradigm for multi-party private set intersection (PSI) that allows parties to compute the intersection 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 any number of semi-honest participants (i.e., without an honest majority).
We demonstrate the practicality of our protocols with an implementation. To the best of our knowledge, this is the first implementation of a multi-party PSI protocol. For 5 parties with data-sets of 220 items each, our protocol requires only 72 seconds. In an optimization achieving a slightly weaker variant of security (augmented semi-honest model), the same task requires only 22 seconds.
The technical core of our protocol is oblivious evaluation of a programmable pseudorandom function (OPPRF), which we instantiate in three different ways. We believe our new OPPRF abstraction and constructions may be of independent interest.
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