29 citations · 36 across the 28 of their papers we have counts for
32 papers
Contraction and Statistical Inference under Privacy for Uniformly Bounded Distributions
Leonhard Grosse, Sara Saeidian, Tobias J. Oechtering +1
We investigate -interior pointwise maximal leakage (PML) as a tool for contraction analyses and disclosure control. Based on the strong adversarial threat models from maximal le…
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 theor…
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…
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…
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…
Risk level dependent Minimax Quantile lower bounds for Interactive Statistical Decision Making
Raghav Bongole, Amirreza Zamani, Tobias J. Oechtering +1
Minimax risk and regret focus on expectation, missing rare failures critical in safety-critical bandits and reinforcement learning. Minimax quantiles capture these tails. Three str…
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…
Privacy Mechanism Design based on Empirical Distributions
Leonhard Grosse, Sara Saeidian, Mikael Skoglund +1
Pointwise maximal leakage (PML) is a per-outcome privacy measure based on threat models from quantitative information flow. Privacy guarantees with PML rely on knowledge about the…
Refined PAC-Bayes Bounds for Offline Bandits
Amaury Gouverneur, Tobias J. Oechtering, Mikael Skoglund
In this paper, we present refined probabilistic bounds on empirical reward estimates for off-policy learning in bandit problems. We build on the PAC-Bayesian bounds from Seldin et…
Integrated Sensing and Communication with Distributed Rate-Limited Helpers
Yiqi Chen, Holger Boche, Tobias J. Oechtering +1
This paper studies integrated sensing and communication (ISAC) systems with two rate-limited helpers who observe the channel state sequence and the feedback sequence, respectively.…
Evaluating Differential Privacy on Correlated Datasets Using Pointwise Maximal Leakage
Sara Saeidian, Tobias J. Oechtering, Mikael Skoglund
Data-driven advancements significantly contribute to societal progress, yet they also pose substantial risks to privacy. In this landscape, differential privacy (DP) has become a c…
An Information-Theoretic Analysis of Thompson Sampling with Infinite Action Spaces
Amaury Gouverneur, Borja Rodriguez Gálvez, Tobias Oechtering +1
This paper studies the Bayesian regret of the Thompson Sampling algorithm for bandit problems, building on the information-theoretic framework introduced by Russo and Van Roy (2015…
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…
Multi-terminal Strong Coordination over Noisy Channels with Encoder Co-operation
Viswanathan Ramachandran, Tobias J. Oechtering, Mikael Skoglund
We investigate the problem of strong coordination over a multiple-access channel (MAC) with cribbing encoders. In this configuration, two encoders observe independent and identical…
Bounds on the privacy amplification of arbitrary channels via the contraction of -divergence
Leonhard Grosse, Sara Saeidian, Tobias J. Oechtering +1
We examine the privacy amplification of channels that do not necessarily satisfy any LDP guarantee by analyzing their contraction behavior in terms of -divergence, an -dive…
Private Semantic Communications with Separate Blind Encoders
Amirreza Zamani, Mikael Skoglund
We study a semantic communication problem with a privacy constraint where an encoder consists of two separate parts, e.g., encoder 1 and encoder 2. The first encoder has access to…
An Information-Theoretic Analysis of Thompson Sampling for Logistic Bandits
Amaury Gouverneur, Borja Rodríguez-Gálvez, Tobias J. Oechtering +1
We study the performance of the Thompson Sampling algorithm for logistic bandit problems. In this setting, an agent receives binary rewards with probabilities determined by a logis…
Multi-terminal Strong Coordination subject to Secrecy Constraints
Viswanathan Ramachandran, Tobias J. Oechtering, Mikael Skoglund
A fundamental problem in decentralized networked systems is to coordinate actions of different agents so that they reach a state of agreement. In such applications, it is additiona…
Information-Theoretic Minimax Regret Bounds for Reinforcement Learning based on Duality
Raghav Bongole, Amaury Gouverneur, Borja Rodríguez-Gálvez +2
We study agents acting in an unknown environment where the agent's goal is to find a robust policy. We consider robust policies as policies that achieve high cumulative rewards for…
Improving Achievability of Cache-Aided Private Variable-Length Coding with Zero Leakage
Amirreza Zamani, Mikael Skoglund
A statistical cache-aided compression problem with a privacy constraint is studied, where a server has access to a database of files, , each of size bits and…
On Information Theoretic Fairness: Compressed Representations With Perfect Demographic Parity
Amirreza Zamani, Borja Rodríguez-Gálvez, Mikael Skoglund
In this article, we study the fundamental limits in the design of fair and/or private representations achieving perfect demographic parity and/or perfect privacy through the lens o…
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…
Multi-Task Private Semantic Communication
Amirreza Zamani, Sajad Daei, Tobias J. Oechtering +1
We study a multi-task private semantic communication problem, in which an encoder has access to an information source arbitrarily correlated with some latent private data. A user h…
Distribution-Preserving Integrated Sensing and Communication with Secure Reconstruction
Yiqi Chen, Tobias Oechtering, Holger Boche +2
Distribution-preserving integrated sensing and communication with secure reconstruction is investigated in this paper. In addition to the distortion constraint, we impose another c…