most citedGroup privacy for personalized federated learning

12 citations · 20 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2022★ 12 cited

Group privacy for personalized federated learning

Filippo Galli, Sayan Biswas, Kangsoo Jung +2

Federated learning (FL) is a type of collaborative machine learning where participating peers/clients process their data locally, sharing only updates to the collaborative model. T…

cs.CR2022

Tight Differential Privacy Blanket for Shuffle Model

Sayan Biswas, Kangsoo Jung, Catuscia Palamidessi

With the recent bloom of focus on digital economy, the importance of personal data has seen a massive surge of late. Keeping pace with this trend, the model of data market is start…

cs.CR2022★ 2 cited

Tight Differential Privacy Guarantees for the Shuffle Model with -Randomized Response

Sayan Biswas, Kangsoo Jung, Catuscia Palamidessi

Most differentially private (DP) algorithms assume a central model in which a reliable third party inserts noise to queries made on datasets, or a local model where the users local…

cs.GT2021★ 1 cited

Establishing the Price of Privacy in Federated Data Trading

Kangsoo Jung, Sayan Biswas, Catuscia Palamidessi

Personal data is becoming one of the most essential resources in today's information-based society. Accordingly, there is a growing interest in data markets, which operate data tra…

cs.GT2021★ 5 cited

An Incentive Mechanism for Trading Personal Data in Data Markets

Sayan Biswas, Kangsoo Jung, Catuscia Palamidessi

With the proliferation of the digital data economy, digital data is considered as the crude oil in the twenty-first century, and its value is increasing. Keeping pace with this tre…