6 citations · 11 across the 8 of their papers we have counts for
8 papers
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…
The Value of Collaboration in Convex Machine Learning with Differential Privacy
Nan Wu, Farhad Farokhi, David Smith +1
In this paper, we apply machine learning to distributed private data owned by multiple data owners, entities with access to non-overlapping training datasets. We use noisy, differe…
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…
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…