Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Zarrabian, Mohammad Amin"'
In this paper, we extend the framework of quantitative information flow (QIF) to include adversaries that use Kolmogorov-Nagumo $f$-mean to infer secrets of a private system. Specifically, in our setting, an adversary uses Kolmogorov-Nagumo $f$-mean
Externí odkaz:
http://arxiv.org/abs/2409.04108
This paper investigates the privacy funnel, a privacy-utility tradeoff problem in which mutual information quantifies both privacy and utility. The objective is to maximize utility while adhering to a specified privacy budget. However, the privacy fu
Externí odkaz:
http://arxiv.org/abs/2408.09659
Information density and its exponential form, known as lift, play a central role in information privacy leakage measures. $\alpha$-lift is the power-mean of lift, which is tunable between the worst-case measure max-lift ($\alpha=\infty$) and more rel
Externí odkaz:
http://arxiv.org/abs/2406.06990
For $\tilde{f}(t) = \exp(\frac{\alpha-1}{\alpha}t)$, this paper proposes a $\tilde{f}$-mean information gain measure. R\'{e}nyi divergence is shown to be the maximum $\tilde{f}$-mean information gain incurred at each elementary event $y$ of channel o
Externí odkaz:
http://arxiv.org/abs/2405.00423
This paper proposes an $\alpha$-leakage measure for $\alpha\in[0,\infty)$ by a cross entropy interpretation of R{\'{e}}nyi entropy. While R\'{e}nyi entropy was originally defined as an $f$-mean for $f(t) = \exp((1-\alpha)t)$, we reveal that it is als
Externí odkaz:
http://arxiv.org/abs/2401.15202
This paper investigates lift, the likelihood ratio between the posterior and prior belief about sensitive features in a dataset. Maximum and minimum lifts over sensitive features quantify the adversary's knowledge gain and should be bounded to protec
Externí odkaz:
http://arxiv.org/abs/2303.01017
This paper proposes a novel watchdog privatization scheme by generalizing local information privacy (LIP) to enhance data utility. To protect the sensitive features $S$ correlated with some useful data $X$, LIP restricts the lift, the ratio of the po
Externí odkaz:
http://arxiv.org/abs/2205.14549
This paper is concerned with enhancing data utility in the privacy watchdog method for attaining information-theoretic privacy. For a specific privacy constraint, the watchdog method filters out the high-risk data symbols through applying a uniform d
Externí odkaz:
http://arxiv.org/abs/2110.04724
This paper proposes an $\alpha$-lift measure for data privacy and determines the optimal privatization scheme that minimizes the $\alpha$-lift in the watchdog method. To release data $X$ that is correlated with sensitive information $S$, the ratio $l
Externí odkaz:
http://arxiv.org/abs/2101.10551
Publikováno v:
Entropy; Apr2023, Vol. 25 Issue 4, p679, 24p
