(Local) Differential Privacy has NO Disparate Impact on Fairness. (arXiv:2304.12845v1 [cs.LG])

In recent years, Local Differential Privacy (LDP), a robust
privacy-preserving methodology, has gained widespread adoption in real-world
applications. With LDP, users can perturb their data on their devices before
sending it out for analysis. However, as the collection of multiple sensitive
information becomes more prevalent across various industries, collecting a
single sensitive attribute under LDP may not be sufficient. Correlated
attributes in the data may still lead to inferences about the sensitive
attribute. This paper empirically studies the impact of …

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