12 citations · 49 across the 13 of their papers we have counts for
13 papers
Mitigating Membership Inference Vulnerability in Personalized Federated Learning
Kangsoo Jung, Sayan Biswas, Catuscia Palamidessi
Federated Learning (FL) has emerged as a promising paradigm for collaborative model training without the need to share clients' personal data, thereby preserving privacy. However,…
Comparing privacy notions for protection against reconstruction attacks in machine learning
Sayan Biswas, Mark Dras, Pedro Faustini +4
Within the machine learning community, reconstruction attacks are a principal concern and have been identified even in federated learning (FL), which was designed with privacy pres…
Federated and differentially private estimation of KL divergence
Sayan Biswas, Graham Cormode, Carsten Maple +1
Measuring distribution drifts is a key task in managing distributed, sensitive data, as it underpins a wide range of federated learning and analytics applications. In many practica…
Bayes' capacity as a measure for reconstruction attacks in federated learning
Sayan Biswas, Mark Dras, Pedro Faustini +4
Within the machine learning community, reconstruction attacks are a principal attack of concern and have been identified even in federated learning, which was designed with privacy…
Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond
Filippo Galli, Kangsoo Jung, Sayan Biswas +2
Federated learning (FL) is a framework for training machine learning models in a distributed and collaborative manner. During training, a set of participating clients process their…
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…
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…
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…