P2KMV: A Privacy-preserving Counting Sketch for Efficient and Accurate Set Intersection Cardinality Estimations

Hagen Sparka; Florian Tschorsch; Björn Scheuermann

Paper 2018/234

P2KMV: A Privacy-preserving Counting Sketch for Efficient and Accurate Set Intersection Cardinality Estimations

Hagen Sparka, Florian Tschorsch, and Björn Scheuermann

Abstract

In this paper, we propose P2KMV, a novel privacy-preserving counting sketch, based on the k minimum values algorithm. With P2KMV, we offer a versatile privacy-enhanced technology for obtaining statistics, following the principle of data minimization, and aiming for the sweet spot between privacy, accuracy, and computational efficiency. As our main contribution, we develop methods to perform set operations, which facilitate cardinality estimates under strong privacy requirements. Most notably, we propose an efficient, privacy-preserving algorithm to estimate the set intersection cardinality. P2KMV provides plausible deniability for all data items contained in the sketch. We discuss the algorithm's privacy guarantees as well as the accuracy of the obtained estimates. An experimental evaluation confirms our analytical expectations and provides insights regarding parameter choices.

Metadata

Available format(s): PDF
Category: Applications
Publication info: Preprint. MINOR revision.
Keywords: privacy
Contact author(s): florian tschorsch @ tu-berlin de
History: 2018-03-05: received
Short URL: https://ia.cr/2018/234
License: CC BY

BibTeX

@misc{cryptoeprint:2018/234,
      author = {Hagen Sparka and Florian Tschorsch and Björn Scheuermann},
      title = {{P2KMV}: A Privacy-preserving Counting Sketch for Efficient and Accurate Set Intersection Cardinality Estimations},
      howpublished = {Cryptology {ePrint} Archive, Paper 2018/234},
      year = {2018},
      url = {https://eprint.iacr.org/2018/234}
}