S
Salman Salamatian
Researcher at Massachusetts Institute of Technology
Publications - 48
Citations - 700
Salman Salamatian is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Linear network coding & Decoding methods. The author has an hindex of 10, co-authored 48 publications receiving 514 citations. Previous affiliations of Salman Salamatian include École Polytechnique Fédérale de Lausanne & Harvard University.
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Proceedings ArticleDOI
From the Information Bottleneck to the Privacy Funnel
TL;DR: It is shown that the privacy-utility tradeoff under the log-loss can be cast as the non-convex Privacy Funnel optimization, and its connection to the Information Bottleneck is Leveraged, to provide a greedy algorithm that is locally optimal.
Proceedings ArticleDOI
How to hide the elephant- or the donkey- in the room: Practical privacy against statistical inference for large data
Salman Salamatian,Amy Zhang,Flavio P. Calmon,Sandilya Bhamidipati,Nadia Fawaz,Branislav Kveton,Pedro Carvalho Oliveira,Nina Taft +7 more
TL;DR: This work reduces the optimization size by introducing a quantization step, and shows how to generate privacy mappings under quantization, and evaluates the method on a dataset showing correlations between political views and TV viewing habits, and demonstrates that good privacy properties can be achieved with limited distortion.
Journal ArticleDOI
Managing Your Private and Public Data: Bringing Down Inference Attacks Against Your Privacy
Salman Salamatian,Amy Zhang,Flavio P. Calmon,Sandilya Bhamidipati,Nadia Fawaz,Branislav Kveton,Pedro Carvalho Oliveira,Nina Taft +7 more
TL;DR: It is demonstrated that good privacy properties can be achieved with limited distortion so as not to undermine the original purpose of the publicly released data, e.g., recommendations.
Journal ArticleDOI
Why Botnets Work: Distributed Brute-Force Attacks Need No Synchronization
TL;DR: In this article, the authors analyzed the impact of distribution and asynchronization on the overall computational effort necessary to breach a system based on guesswork, a measure of the number of queries (guesses) before a correct sequence, such as a password, is found in an optimal attack.
Proceedings ArticleDOI
Task-Based Quantization for Recovering Quadratic Functions Using Principal Inertia Components
TL;DR: This numerical study demonstrates that, when using scalar ADCs, notable performance gains can be achieved using the proposed design over intuitive approaches such as quantizing the quadratic function directly as well as task-ignorant quantization.