Cryptology ePrint Archive: Report 2019/805

RRTxFM: Probabilistic Counting for Differentially Private Statistics

Saskia Nuñez von Voigt and Florian Tschorsch

Abstract: Data minimization has become a paradigm to address privacy concerns when collecting and storing personal data. In this paper we present two new approaches, RSTxFM and RRTxFM, to estimate the cardinality of a dataset while ensuring differential privacy. We argue that privacy-preserving cardinality estimators are able to realize strong privacy requirements. Both approaches are based on a probabilistic counting algorithm which has a logarithmic space complexity. We combine this with a randomization technique to provide differential privacy. In our analysis, we detail the privacy and utility guarantees and expose the impact of the various parameters. Moreover, we discuss workforce analytics as application area where strong privacy is paramount.

Category / Keywords: applications / Probabilistic Counting, Differential Privacy, Randomized Response.

Original Publication (in the same form): I3E 2019: Digital Transformation for a Sustainable Society in the 21st Century

Date: received 11 Jul 2019, last revised 18 Mar 2020

Contact author: saskia nunezvonvoigt at tu-berlin de

Available format(s): PDF | BibTeX Citation

Version: 20200318:171100 (All versions of this report)

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