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Sayan Biswas
Sayan Biswas
Postdoctoral Researcher at EPFL
Verified email at epfl.ch - Homepage
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Year
Group privacy for personalized federated learning
F Galli, S Biswas, K Jung, C Palamidessi, T Cucinotta
9th International Conference on Information Systems Security and Privacy …, 2023
182023
Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond
F Galli, K Jung, S Biswas, C Palamidessi, T Cucinotta
Springer Nature Computer Science 4 (6), Article 831 (2023), 2023
102023
PRIVIC: A privacy-preserving method for incremental collection of location data
S Biswas, C Palamidessi
Proceedings on Privacy Enhancing Technologies (PoPETs) 2024 (1), 582–596, 2022
102022
An incentive mechanism for trading personal data in data markets
S Biswas, K Jung, C Palamidessi
Theoretical Aspects of Computing–ICTAC 2021: 18th International Colloquium …, 2021
72021
Noiseless privacy-preserving decentralized learning
S Biswas, M Even, AM Kermarrec, L Massoulié, R Pires, R Sharma, ...
Proceedings on Privacy Enhancing Technologies (PoPETs) 2025 (1), 824–844, 2025
5*2025
A privacy-preserving querying mechanism with high utility for electric vehicles
UI Atmaca, S Biswas, C Maple, C Palamidessi
IEEE Open Journal of Vehicular Technology 5, 262-277, 2024
52024
Establishing the price of privacy in federated data trading
K Jung, S Biswas, C Palamidessi
Protocols, Strands, and Logic: Essays Dedicated to Joshua Guttman on the …, 2021
52021
Low-cost privacy-aware decentralized learning
S Biswas, D Frey, R Gaudel, AM Kermarrec, D Lerévérend, R Pires, ...
arXiv preprint arXiv:2403.11795, 2024
22024
Understanding and optimizing the trade-off between privacy and utility from a foundational perspective
S Biswas
Institut Polytechnique de Paris, 2023
22023
Boosting Asynchronous Decentralized Learning with Model Fragmentation
S Biswas, AM Kermarrec, A Marouani, R Pires, R Sharma, M De Vos
arXiv preprint arXiv:2410.12918, 2024
12024
Bayes' capacity as a measure for reconstruction attacks in federated learning
S Biswas, M Dras, P Faustini, N Fernandes, A McIver, C Palamidessi, ...
Protect-IT Workshop at the 37th IEEE Computer Security Foundations Symposium …, 2024
12024
Tight Differential Privacy Guarantees for the Shuffle Model with k-Randomized Response
S Biswas, K Jung, C Palamidessi
International Symposium on Foundations and Practice of Security, 440-458, 2023
12023
Mitigating Membership Inference Vulnerability in Personalized Federated Learning
K Jung, S Biswas, C Palamidessi
arXiv preprint arXiv:2503.09414, 2025
2025
Comparing privacy notions for protection against reconstruction attacks in machine learning
S Biswas, M Dras, P Faustini, N Fernandes, A McIver, C Palamidessi, ...
arXiv preprint arXiv:2502.04045, 2025
2025
Distributed, communication-efficient, and differentially private estimation of KL divergence
M Scott, S Biswas, G Cormode, C Maple
arXiv preprint arXiv:2411.16478, 2024
2024
Fair Decentralized Learning
S Biswas, AM Kermarrec, R Sharma, T Trinca, M de Vos
arXiv preprint arXiv:2410.02541, 2024
2024
Secure Aggregation Meets Sparsification in Decentralized Learning
S Biswas, AM Kermarrec, R Pires, R Sharma, M Vujasinovic
arXiv preprint arXiv:2405.07708, 2024
2024
Impact of Sampling on Locally Differentially Private Data Collection
S Biswas, G Cormode, C Maple
Competitive Advantage in the Digital Economy (CADE 2022), 64-70, 2022
2022
Tight Differential Privacy Blanket for Shuffle Model
S Biswas, K Jung, C Palamidessi
Competitive Advantage in the Digital Economy (CADE 2022), 61-63, 2022
2022
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