Unbalanced private set intersection cardinality protocol with low communication cost

Abstract Private set intersection cardinality (PSI-CA) allows two parties, the sender and receiver, to compute the cardinality of the intersection, without revealing anything more to the other party. This paper focuses on the unbalanced private data sets case, where two parties hold sets of private data items, such as the users’ identifiers; and where the size of the receiver’s private data set is significantly smaller than the size of the sender’s private data set. Two parties want to learn the cardinality of the intersection, but nothing else. The commutative encryption inspires authors to develop a novel protocol to solve the problem. Furthermore, by the application of the Bloom filter, the receiver can compute the output more easily than by the method that the encryption is carried out on the sender’s private data set when low-power mobile IoT devices are used. In the semi-honest model, we can prove the security of our protocol when the sender’s data set is big enough. The experiment shows the deviation of our protocol is negligible and the computation costs of our protocol.

[1]  Rolf Egert,et al.  Privately Computing Set-Union and Set-Intersection Cardinality via Bloom Filters , 2015, ACISP.

[2]  Emiliano De Cristofaro,et al.  Linear-Complexity Private Set Intersection Protocols Secure in Malicious Model , 2010, ASIACRYPT.

[3]  Jan Camenisch,et al.  Fair Private Set Intersection with a Semi-trusted Arbiter , 2013, IACR Cryptol. ePrint Arch..

[4]  Panagiotis Papadimitratos,et al.  Privacy-Preserving Relationship Path Discovery in Social Networks , 2009, CANS.

[5]  Moti Yung From Mental Poker to Core Business: Why and How to Deploy Secure Computation Protocols? , 2015, CCS.

[6]  Yehuda Lindell,et al.  Secure Two-Party Computation via Cut-and-Choose Oblivious Transfer , 2011, Journal of Cryptology.

[7]  Dan Boneh,et al.  Location Privacy via Private Proximity Testing , 2011, NDSS.

[8]  Burton H. Bloom,et al.  Space/time trade-offs in hash coding with allowable errors , 1970, CACM.

[9]  Jonathan Katz,et al.  Private Set Intersection: Are Garbled Circuits Better than Custom Protocols? , 2012, NDSS.

[10]  Moti Yung,et al.  Efficient robust private set intersection , 2009, Int. J. Appl. Cryptogr..

[11]  Martin E. Hellman,et al.  An improved algorithm for computing logarithms over GF(p) and its cryptographic significance (Corresp.) , 1978, IEEE Trans. Inf. Theory.

[12]  Emiliano De Cristofaro,et al.  Practical Private Set Intersection Protocols with Linear Complexity , 2010, Financial Cryptography.

[13]  Dawn Xiaodong Song,et al.  Privacy-Preserving Set Operations , 2005, CRYPTO.

[14]  Sasu Tarkoma,et al.  Theory and Practice of Bloom Filters for Distributed Systems , 2012, IEEE Communications Surveys & Tutorials.

[15]  Benny Pinkas,et al.  Keyword Search and Oblivious Pseudorandom Functions , 2005, TCC.

[16]  Benny Pinkas,et al.  Efficient Private Matching and Set Intersection , 2004, EUROCRYPT.

[17]  Yehuda Lindell,et al.  Efficient Protocols for Set Intersection and Pattern Matching with Security Against Malicious and Covert Adversaries , 2008, TCC.

[18]  Jan Camenisch,et al.  Private Intersection of Certified Sets , 2009, Financial Cryptography.

[19]  Constantin Zopounidis,et al.  Multi-group discrimination using multi-criteria analysis: Illustrations from the field of finance , 2002, Eur. J. Oper. Res..