Privacy-Utility Tradeoff Based on α-lift
Mohammad Amin Zarrabian, Parastoo Sadeghi · 2024
Information density and its exponential form, known as lift, play a central role in information privacy leakage measures. a-lift is the power-mean of lift, which is tunable between the worst-case measure max-lift$(\alpha=\infty)$and more relaxed versions ($\alpha < \infty)$. This paper investigates the optimization problem of the privacy-utility tradeoff (PUT) where$\alpha$-lift and mutual information are privacy and utility measures, respectively. Due to the nonlinear nature of a-lift for$\alpha < \infty$, finding the optimal solution is challenging. Therefore, we propose a heuristic algorithm to estimate the optimal utility for each value of$\alpha$. inspired by the optimal solution for$\alpha=\infty$and the convexity of$\alpha$- lift with respect to the lift, which we prove. The numerical results show the superiority of the algorithm compared to a previous algorithm in the literature and indicate the effective range of$\alpha$and privacy budget ε with good PUT performance.