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(Local) Differential Privacy has NO Disparate Impact on Fairness. (arXiv:2304.12845v1 [cs.LG])
cs.CR updates on arXiv.org arxiv.org
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 …
adoption analysis applications attributes collecting collection data devices differential privacy fairness impact information local may privacy sensitive information single studies under world