15 papers
Exact Privacy--Distortion Design of Support-Constrained Randomized Response
Amirreza Zamani, Parastoo Sadeghi, Mikael Skoglund
Full-support local randomizers can incur unnecessary reporting distortion on ordered alphabets. We study randomized response whose output is restricted to a radius- neighborhood…
Residual-Information Generalization Bounds Under Selective Privacy: Extended Version
Amirreza Zamani, Parastoo Sadeghi, Mikael Skoglund
Information-theoretic generalization bounds usually control dependence on the task sample, whereas selective privacy may constrain only a different, private coordinate. We study i.…
Sparse Kernel Mechanisms for Locally Differentially Private Discrete Channels
Amirreza Zamani, Parastoo Sadeghi, Mikael Skoglund
We study sparse locally private discrete mechanisms generated by a nonnegative kernel and an input-dependent admissible output support. The support set is intentionally small relat…
Sparse Point-wise Privacy Leakage: Mechanism Design and Fundamental Limits
Amirreza Zamani, Sajad Daei, Parastoo Sadeghi +1
We study an information-theoretic privacy mechanism design problem, where an agent observes useful data that is arbitrarily correlated with sensitive data , and design discl…
Privacy-Utility Trade-offs Under Multi-Level Point-Wise Leakage Constraints
Amirreza Zamani, Parastoo Sadeghi, Mikael Skoglund
An information-theoretic privacy mechanism design is studied, where an agent observes useful data which is correlated with the private data . The agent wants to reveal the i…
An Information Geometric Approach to Local Information Privacy with Applications to Max-lift and Local Differential Privacy
Amirreza Zamani, Parastoo Sadeghi, Mikael Skoglund
We study an information-theoretic privacy mechanism design, where an agent observes useful data and wants to reveal the information to a user. Since the useful data is correlat…
An Extension of the Adversarial Threat Model in Quantitative Information Flow
Mohammad Amin Zarrabian, Parastoo Sadeghi
In this paper, we propose an extended framework for quantitative information flow (QIF), aligned with the previously proposed core-concave generalization of entropy measures, to in…
An Algorithm for Enhancing Privacy-Utility Tradeoff in the Privacy Funnel and Other Lift-based Measures
Mohammad Amin Zarrabian, Parastoo Sadeghi
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…
-leakage by Rényi Divergence and Sibson Mutual Information
Ni Ding, Mohammad Amin Zarrabian, Parastoo Sadeghi
For , this paper proposes a -mean information gain measure. Rényi divergence is shown to be the maximum -mean information g…
Information Density Bounds for Privacy
Sara Saeidian, Leonhard Grosse, Parastoo Sadeghi +2
This paper explores the implications of guaranteeing privacy by imposing a lower bound on the information density between the private and the public data. We introduce a novel and…
Privacy-Utility Tradeoff Based on -lift
Mohammad Amin Zarrabian, Parastoo Sadeghi
Information density and its exponential form, known as lift, play a central role in information privacy leakage measures. -lift is the power-mean of lift, which is tunable betwe…
Quantifying Privacy via Information Density
Leonhard Grosse, Sara Saeidian, Parastoo Sadeghi +2
We examine the relationship between privacy metrics that utilize information density to measure information leakage between a private and a disclosed random variable. Firstly, we p…
A Cross Entropy Interpretation of R{é}nyi Entropy for -leakage
Ni Ding, Mohammad Amin Zarrabian, Parastoo Sadeghi
This paper proposes an -leakage measure for by a cross entropy interpretation of R{é}nyi entropy. While Rényi entropy was originally defined as an -mean for…
Optimal Binary Differential Privacy via Graphs
Sahel Torkamani, Javad B. Ebrahimi, Parastoo Sadeghi +2
We present the notion of \emph{reasonable utility} for binary mechanisms, which applies to all utility functions in the literature. This notion induces a partial ordering on the pe…
Generalized Rainbow Differential Privacy
Yuzhou Gu, Ziqi Zhou, Onur Günlü +4
We study a new framework for designing differentially private (DP) mechanisms via randomized graph colorings, called rainbow differential privacy. In this framework, datasets are n…