Linear Program Reconstruction in Practice

Aloni Cohen; Kobbi Nissim

doi:10.29012/jpc.711

PDF Data and Code

Published: Jan 14, 2020

DOI: https://doi.org/10.29012/jpc.711

Keywords:

data privacy, reconstruction attacks

Aloni Cohen

Boston University

https://orcid.org/0000-0002-3492-2447

Kobbi Nissim

Department of Computer Science, Georgetown University

https://orcid.org/0000-0002-6632-8645

Abstract

We briefly report on a successful linear program reconstruction attack performed on a production statistical queries system and using a real dataset. The attack was deployed in test environment in the course of the Aircloak Challenge bug bounty program and is based on the reconstruction algorithm of Dwork, McSherry, and Talwar. We empirically evaluate the effectiveness of the algorithm and a related algorithm by Dinur and Nissim with various dataset sizes, error rates, and numbers of queries in a Gaussian noise setting.

How to Cite

Cohen, Aloni, and Kobbi Nissim. 2020. “Linear Program Reconstruction in Practice”. Journal of Privacy and Confidentiality 10 (1). https://doi.org/10.29012/jpc.711.

Issue

Vol. 10 No. 1 (2020): Special Issue: Theory and Practice of Differential Privacy (TPDP 2018)

Section

TPDP 2018

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Funding data

National Science Foundation
Grant numbers CNS-1413920;Graduate Student Research Fellowship
Facebook
Grant numbers 2018 Fellowship

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The Journal of Privacy and Confidentiality is an open-access multi-disciplinary journal whose purpose is to facilitate the coalescence of research methodologies and activities in the areas of privacy, confidentiality, and disclosure limitation. The JPC seeks to publish a wide range of research and review papers, not only from academia, but also from government (especially official statistical agencies) and industry, and to serve as a forum for exchange of views, discussion, and news. For more information, see the About the Journal page.