Privacy-Utility Tradeoff Based on $α$-lift
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866911928406769664 |
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| author | Zarrabian, Mohammad Amin Sadeghi, Parastoo |
| author_facet | Zarrabian, Mohammad Amin Sadeghi, Parastoo |
| contents | Information density and its exponential form, known as lift, play a central role in information privacy leakage measures. $α$-lift is the power-mean of lift, which is tunable between the worst-case measure max-lift ($α=\infty$) and more relaxed versions ($α<\infty$). This paper investigates the optimization problem of the privacy-utility tradeoff (PUT) where $α$-lift and mutual information are privacy and utility measures, respectively. Due to the nonlinear nature of $α$-lift for $α<\infty$, finding the optimal solution is challenging. Therefore, we propose a heuristic algorithm to estimate the optimal utility for each value of $α$, inspired by the optimal solution for $α=\infty$ and the convexity of $α$-lift with respect to the lift, which we prove. The numerical results show the efficacy of the algorithm and indicate the effective range of $α$ and privacy budget $\varepsilon$ with good PUT performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06990 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Privacy-Utility Tradeoff Based on $α$-lift Zarrabian, Mohammad Amin Sadeghi, Parastoo Information Theory Information density and its exponential form, known as lift, play a central role in information privacy leakage measures. $α$-lift is the power-mean of lift, which is tunable between the worst-case measure max-lift ($α=\infty$) and more relaxed versions ($α<\infty$). This paper investigates the optimization problem of the privacy-utility tradeoff (PUT) where $α$-lift and mutual information are privacy and utility measures, respectively. Due to the nonlinear nature of $α$-lift for $α<\infty$, finding the optimal solution is challenging. Therefore, we propose a heuristic algorithm to estimate the optimal utility for each value of $α$, inspired by the optimal solution for $α=\infty$ and the convexity of $α$-lift with respect to the lift, which we prove. The numerical results show the efficacy of the algorithm and indicate the effective range of $α$ and privacy budget $\varepsilon$ with good PUT performance. |
| title | Privacy-Utility Tradeoff Based on $α$-lift |
| topic | Information Theory |
| url | https://arxiv.org/abs/2406.06990 |