Towards Practical Differential Privacy in Data Analysis: Understanding the Effect of Epsilon on Utility in Private ERM. (arXiv:2206.03488v1 [cs.CR])

In this paper, we focus our attention on private Empirical Risk Minimization
(ERM), which is one of the most commonly used data analysis method. We take the
first step towards solving the above problem by theoretically exploring the
effect of epsilon (the parameter of differential privacy that determines the
strength of privacy guarantee) on utility of the learning model. We trace the
change of utility with modification of epsilon and reveal an established
relationship between epsilon and utility. We then formalize this relationship
and propose a practical approach for estimating the utility under an arbitrary
value of epsilon. Both theoretical analysis and experimental results
demonstrate high estimation accuracy and broad applicability of our approach in
practical applications. As providing algorithms with strong utility guarantees
that also give privacy when possible becomes more and more accepted, our
approach would have high practical value and may be likely to be adopted by
companies and organizations that would like to preserve privacy but are
unwilling to compromise on utility.



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