12 citations · 49 across the 13 of their papers we have counts for
6 papers · 1 filter
Impact of Sampling on Locally Differentially Private Data Collection
Sayan Biswas, Graham Cormode, Carsten Maple
With the recent bloom of data, there is a huge surge in threats against individuals' private information. Various techniques for optimizing privacy-preserving data analysis are at…
PRIVIC: A privacy-preserving method for incremental collection of location data
Sayan Biswas, Catuscia Palamidessi
With recent advancements in technology, the threats of privacy violations of individuals' sensitive data are surging. Location data, in particular, have been shown to carry a subst…
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…
A privacy preserving querying mechanism with high utility for electric vehicles
Ugur Ilker Atmaca, Sayan Biswas, Carsten Maple +1
Electric vehicles (EVs) are gaining popularity due to the growing awareness for a sustainable future. However, since there are disproportionately fewer charging stations than EVs,…
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…
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…