17 papers
Dobrushin Coefficients of Private Mechanisms Beyond Local Differential Privacy
Leonhard Grosse, Sara Saeidian, Tobias J. Oechtering +1
We investigate Dobrushin coefficients of discrete Markov kernels that have bounded pointwise maximal leakage (PML) with respect to all distributions with a minimum probability mass…
Minimax Quantile Lower Bounds for Interactive Statistical Decision Making with Privacy
Raghav Bongole, Amirreza Zamani, Tobias J. Oechtering +1
Minimax risk and regret are expectation-based criteria and do not capture rare but consequential failures. To address this concern, we develop a -explicit minimax-quantile theo…
Privacy Guarantee for Nash Equilibrium Computation of Aggregative Games Based on Pointwise Maximal Leakage
Zhaoyang Cheng, Guanpu Chen, Tobias J. Oechtering +1
Privacy preservation has served as a key metric in designing Nash equilibrium (NE) computation algorithms. Although differential privacy (DP) has been widely employed for privacy g…
Instantiating Bayesian CVaR lower bounds in Interactive Decision Making Problems
Raghav Bongole, Tobias J. Oechtering, Mikael Skoglund
Recent work established a generalized-Fano framework for lower bounding prior-predictive (Bayesian) CVaR in interactive statistical decision making. In this paper, we show how to i…
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…
Generalizing the Fano inequality further
Raghav Bongole, Tobias J. Oechtering, Mikael Skoglund
Interactive statistical decision making (ISDM) features algorithm-dependent data generated through interaction. Existing information-theoretic lower bounds in ISDM largely target e…