Personalized Differential Privacy for Ridge Regression. (arXiv:2401.17127v1 [cs.LG])

The increased application of machine learning (ML) in sensitive domains
requires protecting the training data through privacy frameworks, such as
differential privacy (DP). DP requires to specify a uniform privacy level
$\varepsilon$ that expresses the maximum privacy loss that each data point in
the entire dataset is willing to tolerate. Yet, in practice, different data
points often have different privacy requirements. Having to set one uniform
privacy level is usually too restrictive, often forcing a learner to guarantee
the stringent …

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