10 citations · 22 across the 16 of their papers we have counts for
17 papers
Learn to Unlearn: A Survey on Machine Unlearning
Youyang Qu, Xin Yuan, Ming Ding +3
Machine Learning (ML) models have been shown to potentially leak sensitive information, thus raising privacy concerns in ML-driven applications. This inspired recent research on re…
PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels
Huaxi Huang, Hui Kang, Sheng Liu +4
Convolutional Neural Networks (CNNs) have demonstrated superiority in learning patterns, but are sensitive to label noises and may overfit noisy labels during training. The early s…
Enhancing Utility in the Watchdog Privacy Mechanism
Mohammad Amin Zarrabian, Ni Ding, Parastoo Sadeghi +1
This paper is concerned with enhancing data utility in the privacy watchdog method for attaining information-theoretic privacy. For a specific privacy constraint, the watchdog meth…
Private Graph Data Release: A Survey
Yang Li, Michael Purcell, Thierry Rakotoarivelo +3
The application of graph analytics to various domains has yielded tremendous societal and economical benefits in recent years. However, the increasingly widespread adoption of grap…
Realistic Differentially-Private Transmission Power Flow Data Release
David Smith, Frederik Geth, Elliott Vercoe +5
For the modeling, design and planning of future energy transmission networks, it is vital for stakeholders to access faithful and useful power flow data, while provably maintaining…
On Properties and Optimization of Information-theoretic Privacy Watchdog
Parastoo Sadeghi, Ni Ding, Thierry Rakotoarivelo
We study the problem of privacy preservation in data sharing, where is a sensitive variable to be protected and is a non-sensitive useful variable correlated with . Vari…
Privacy-Utility Tradeoff in a Guessing Framework Inspired by Index Coding
Yucheng Liu, Ni Ding, Parastoo Sadeghi +1
This paper studies the tradeoff in privacy and utility in a single-trial multi-terminal guessing (estimation) framework using a system model that is inspired by index coding. There…
Part II: A Practical Approach for Successive Omniscience
Ni Ding, Parastoo Sadeghi, Thierry Rakotoarivelo
In Part I, we studied the communication for omniscience (CO) problem and proposed a parametric (PAR) algorithm to determine the minimum sum-rate at which a set of users indexed by…
Part I: Improving Computational Efficiency of Communication for Omniscience
Ni Ding, Parastoo Sadeghi, Thierry Rakotoarivelo
Communication for omniscience (CO) refers to the problem where the users in a finite set observe a discrete multiple random source and want to exchange data over broadcast chan…
Improving Computational Efficiency of Communication for Omniscience and Successive Omniscience
Ni Ding, Parastoo Sadeghi, Thierry Rakotoarivelo
For a group of users in where everyone observes a component of a discrete multiple random source, the process that users exchange data so as to reach omniscience, the state whe…
Attaining Fairness in Communication for Omniscience
Ni Ding, Parastoo Sadeghi, David Smith +1
This paper studies how to attain fairness in communication for omniscience, where a set of users exchange their observations of a discrete multiple random source to attain omniscie…
Differentially Private Release of High-Dimensional Datasets using the Gaussian Copula
Hassan Jameel Asghar, Ming Ding, Thierry Rakotoarivelo +2
We propose a generic mechanism to efficiently release differentially private synthetic versions of high-dimensional datasets with high utility. The core technique in our mechanism…
Fairness in Multiterminal Data Compression: A Splitting Method for The Egalitarian Solution
Ni Ding, David Smith, Parastoo Sadeghi +1
This paper proposes a novel splitting (SPLIT) algorithm to achieve fairness in the multiterminal lossless data compression problem. It finds the egalitarian solution in the Slepian…
Fairness in Multiterminal Data Compression: Decomposition of Shapley Value
Ni Ding, David Smith, Parastoo Sadeghi +1
We consider the problem of how to determine a fair source coding rate allocation method for the lossless data compression problem in multiterminal networks, e.g, the wireless senso…
Distributed Data Compression in Sensor Clusters: A Maximum Independent Flow Approach
Ni Ding, Parastoo Sadeghi, David Smith +1
Let a cluster (network) of sensors be connected by the communication links, each link having a capacity upper bound. Each sensor observes a discrete random variable in private and…
Repeatable Experiments with LabWiki
Thierry Rakotoarivelo, Guillaume Jourjon, Olivier Mehani +2
The ability to repeat the experiments from a research study and obtain similar results is a corner stone in experiment-based scientific discovery. This essential feature has been o…
A Method for the Characterisation of Observer Effects and its Application to OML
Olivier Mehani, Guillaume Jourjon, Thierry Rakotoarivelo
In all measurement campaigns, one needs to assert that the instrumentation tools do not significantly impact the system being monitored. This is critical to future claims based on…