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20172021
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cs.IT2021

Optimal Maximal Leakage-Distortion Tradeoff

Sara Saeidian, Giulia Cervia, Tobias J. Oechtering +1

Most methods for publishing data with privacy guarantees introduce randomness into datasets which reduces the utility of the published data. In this paper, we study the privacy-uti…

cs.IT2021

Fixed-Length Strong Coordination Capacity

Giulia Cervia, Tobias J. Oechtering, Mikael Skoglund

This paper investigates the problem of synthesizing joint distributions in the finite-length regime. For a fixed blocklength and an upper bound on the distribution approximatio…

cs.IT2020

Quantifying Membership Privacy via Information Leakage

Sara Saeidian, Giulia Cervia, Tobias J. Oechtering +1

Machine learning models are known to memorize the unique properties of individual data points in a training set. This memorization capability can be exploited by several types of a…

cs.IT2020

Secure Strong Coordination

Giulia Cervia, German Bassi, Mikael Skoglund

We consider a network of two nodes separated by a noisy channel, in which the source and its reconstruction have to be strongly coordinated, while simultaneously satisfying the str…

cs.IT2020

Remote Joint Strong Coordination and Reliable Communication

Giulia Cervia, Tobias J. Oechtering, Mikael Skoglund

We consider a three-node network, in which two agents wish to communicate over a noisy channel, while controlling the distribution observed by a third external agent. We use strong…

cs.IT2019

Fixed-Length Strong Coordination

Giulia Cervia, Tobias Oechtering, Mikael Skoglund

We consider the problem of synthesizing joint distributions of signals and actions over noisy channels in the finite-length regime. For a fixed blocklength and an upper bound o…