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The Limits of Differential Privacy (and its Misuse in Data Release and Machine Learning)
November 04, 2020 Β· Declared Dead Β· π Communications of the ACM
"No code URL or promise found in abstract"
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Authors
Josep Domingo-Ferrer, David SΓ‘nchez, Alberto Blanco-Justicia
arXiv ID
2011.02352
Category
cs.CR: Cryptography & Security
Citations
124
Venue
Communications of the ACM
Last Checked
4 months ago
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.
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