Paper 2013/515

When Private Set Intersection Meets Big Data: An Efficient and Scalable Protocol

Changyu Dong, Liqun Chen, and Zikai Wen

Abstract

Large scale data processing brings new challenges to the design of privacy-preserving protocols: how to meet the increasing requirements of speed and throughput of modern applications, and how to scale up smoothly when data being protected is big. Efficiency and scalability become critical criteria for privacy preserving protocols in the age of Big Data. In this paper, we present a new Private Set Intersection (PSI) protocol that is extremely efficient and highly scalable compared with existing protocols. The protocol is based on a novel approach that we call oblivious Bloom intersection. It has linear complexity and relies mostly on efficient symmetric key operations. It has high scalability due to the fact that most operations can be parallelized easily. The protocol has two versions: a basic protocol and an enhanced protocol, the security of the two variants is analyzed and proved in the semi-honest model and the malicious model respectively. A prototype of the basic protocol has been built. We report the result of performance evaluation and compare it against the two previously fastest PSI protocols. Our protocol is orders of magnitude faster than these two protocols. To compute the intersection of two million-element sets, our protocol needs only 41 seconds (80-bit security) and 339 seconds (256-bit security) on moderate hardware in parallel mode.

Note: Added a note regarding selective failure attacks.

Metadata
Available format(s)
PDF
Category
Cryptographic protocols
Publication info
Published elsewhere. MAJOR revision.ACM CCS 2013
Contact author(s)
changyu dong @ gmail com
History
2016-08-04: last of 3 revisions
2013-08-21: received
See all versions
Short URL
https://ia.cr/2013/515
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2013/515,
      author = {Changyu Dong and Liqun Chen and Zikai Wen},
      title = {When Private Set Intersection Meets Big Data: An Efficient and Scalable Protocol},
      howpublished = {Cryptology ePrint Archive, Paper 2013/515},
      year = {2013},
      note = {\url{https://eprint.iacr.org/2013/515}},
      url = {https://eprint.iacr.org/2013/515}
}
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