Ni Ding

Improving Computational Efficiency of Communication for Omniscience and Successive Omniscience

Ni Ding, Parastoo Sadeghi, Thierry Rakotoarivelo

Journal article  |  2021  |  IEEE TRANSACTIONS ON INFORMATION THEORY

Communication for omniscience (CO) refers to the problem where the users in a finite set V observe a discrete multiple random sour..

On Properties and Optimization of Information-theoretic Privacy Watchdog

P Sadeghi, N Ding, T Rakotoarivelo

Conference Proceedings  |  2021  |  2020 IEEE Information Theory Workshop, ITW 2020

We study the problem of privacy preservation in data sharing, where S is a sensitive variable to be protected and X is a non-sensi..

Measuring Information Leakage in Non-stochastic Brute-Force Guessing

Farhad Farokhi, Ni Ding

Conference Proceedings  |  2021  |  2020 IEEE Information Theory Workshop (ITW)

We propose an operational measure of information leakage in a non-stochastic setting to formalize privacy against a brute-force gu..

A Linear Reduction Method for Local Differential Privacy and Log-lift

Ni Ding, Yucheng Liu, Farhad Farokhi

Conference Proceedings  |  2021  |  2021 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY (ISIT)

This paper considers the problem of publishing data X while protecting the correlated sensitive information S. We propose a linear..

alpha-Information-theoretic Privacy Watch- dog and Optimal Privatization Scheme

Ni Ding, Mohammad Amin Zarrabian, Parastoo Sadeghi

Conference Proceedings  |  2021  |  Proceeding of IEEE International Symposium on Information Theory

This paper proposes an α-lift measure for data privacy and determines the optimal privatization scheme that minimizes the α-lift i..

Developing Non-Stochastic Privacy-Preserving Policies Using Agglomerative Clustering

Ni Ding, Farhad Farokhi

Journal article  |  2020  |  IEEE Transactions on Information Forensics and Security

We consider a non-stochastic privacy-preserving problem in which an adversary aims to infer sensitive information S from publicly ..

Privacy-Utility Tradeoff in a Guessing Framework Inspired by Index Coding

Y Liu, N Ding, P Sadeghi, T Rakotoarivelo

Conference Proceedings  |  2020  |  IEEE International Symposium on Information Theory - Proceedings

This paper studies the tradeoff in privacy and utility in a single-trial multi-terminal guessing (estimation) framework using a sy..

Evolutionary Games for Correlation-Aware Clustering in Massive Machine-to-Machine Networks

Nicole Sawyer, Mehdi Naderi Soorki, Walid Saad, David B Smith, Ni Ding

Journal article  |  2019  |  IEEE Transactions on Communications

In this paper, the problem of self-organizing, correlation-aware clustering is studied for a dense network of machine-type devices..

A Submodularity-based Clustering Algorithm for the Information Bottleneck and Privacy Funnel

Ni Ding, Parastoo Sadeghi

Conference Proceedings  |  2019  |  2019 IEEE Information Theory Workshop (ITW)

For the relevant data S that nests in the observation X, the information bottleneck (IB) aims to encode X into X̂ in X, with order..

Improving Computational Efficiency of Communication for Omniscience and Successive Omniscience

N Ding, P Sadeghi, T Rakotoarivelo

Conference Proceedings  |  2019  |  2018 56th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2018

For a group of users in V where everyone observes a component of a discrete multiple random source, the process that users exchang..