The Limits of Differential Privacy (and its Misuse in Data Release and Machine Learning)
Cryptography and Security
2020-11-05 v1
Abstract
Differential privacy (DP) is a neat privacy definition that can co-exist with certain well-defined data uses in the context of interactive queries. However, DP is neither a silver bullet for all privacy problems nor a replacement for all previous privacy models. In fact, extreme care should be exercised when trying to extend its use beyond the setting it was designed for. This paper reviews the limitations of DP and its misuse for individual data collection, individual data release, and machine learning.
Keywords
Cite
@article{arxiv.2011.02352,
title = {The Limits of Differential Privacy (and its Misuse in Data Release and Machine Learning)},
author = {Josep Domingo-Ferrer and David Sánchez and Alberto Blanco-Justicia},
journal= {arXiv preprint arXiv:2011.02352},
year = {2020}
}
Comments
Communications of the ACM, to appear