Sayan Biswas

About Me

I am currently a Postdoctoral Researcher at the SaCS lab at EPFL, based in Lausanne, Switzerland. Recently, I completed my PhD in Computer Science at INRIA and École Polytechnique in France, under the supervision of Catuscia Palamidessi. Previously, I have been a Visiting Scholar at the School of Computing at Macquarie University in Sydney, Australia, collaborating with Annabelle McIver and Natasha Fernandes, and at WMG, the University of Warwick in Coventry, England, working with Carsten Maple and Graham Cormode.

My work primarily involves designing privacy-preserving techniques for analysing data and training models, with a focus on investigating and enhancing the trade-off between privacy and utility from a foundational perspective. In particular, my current research interests hover around:

  • Differential Privacy
  • Federated Learning and, in general, Privacy-Preserving Machine Learning
  • Location Privacy
  • Privacy-Utility Optimization

I study, talk, and do mathematics most of the time; when not, I am typically immersed in chess, cricket, puzzles, or stand-up comedy. Of late, I have got myself back into the habit of (non-academic) reading in my leisure and learning about (often, apparently, useless) facts from across the realms of life. I am trying to learn French with limited success so far. The list of some of my currently favourite results and theorems in mathematics (a list that, obviously, everyone should maintain) include:

  1. The limit of the derangement probability of a set as its size approaches infinity is e 2
  2. Gershgorin circle theorem
  3. Sleeping Beauty problem
  4. Banach–Tarski paradox
  5. “A Drunk Man Will Find His Way Home but a Drunk Bird May Get Lost Forever”

Sayan Biswas

sayan.biswas@epfl.ch or bizwas05@gmail.com

EPFL IC IINFCOM SACS
BC 166 (Bâtiment BC)
Station 14
CH-1015 Lausanne, Switzerland.

Publications


Peer-reviewed conferences

  • (2023) Sayan Biswas, Catuscia Palamidessi: “PRIVIC: A privacy-preserving method for incremental collection of location data”. Proceedings on Privacy Enhancing Technologies (PoPETs), Volume 2024, Issue 1, pp 582-596. Published: October, 2023. PDF , DOI: 10.56553/popets-2024-0033

  • (2023) Filippo Galli, Sayan Biswas, Kangsoo Jung, Tommaso Cucinotta, Catuscia Palamidessi: “Group privacy for Personalized Federated Learning”. Proceedings of the 9th International Conference on Information Systems Security and Privacy (ICISSP) 2023, pp 252-263, SciTePress Digital Library. Published: February 24, 2023. PDF , DOI: 10.5220/0011885000003405

  • (2022) Sayan Biswas, Graham Cormode, Carsten Maple: “Impact of Sampling on Locally Differentially Private Data Collection”. Proceedings of the 8th Competitive Advantage in the Digital Economy -- Resilience, Sustainability, Responsibility, and Identity (CADE) 2022, pp 64-70, IET Digital Library and IEEE Xplore. Published: November 9, 2022. Winner of the Best Paper Award. PDF , DOI: 10.1049/icp.2022.2042

  • (2022) Sayan Biswas, Kangsoo Jung, Catuscia Palamidessi: “Tight Differential Privacy Blanket for Shuffle Model”. Proceedings of the 8th Competitive Advantage in the Digital Economy -- Resilience, Sustainability, Responsibility, and Identity (CADE) 2022, pp 61-63, IET Digital Library and IEEE Xplore. Published: November 9, 2022. PDF , DOI: 10.1049/icp.2022.2041

  • (2021) Sayan Biswas, Kangsoo Jung, Catuscia Palamidessi: “An Incentive Mechanism for Trading Personal Data in Data Markets”. Proceedings of the 18th International Colloquium on Theoretical Aspects of Computing (ICTAC) 2021, pp 197-213, LNCS 12819, Springer. Published: August 20, 2021. PDF , DOI: 10.1007/978-3-030-85315-0_12

Journals

  • (2023) Filippo Galli, Kangsoo Jung, Sayan Biswas, Catuscia Palamidessi, Tommaso Cucinotta: “ Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond”. Springer Nature Computer Science, Volume 4, Issue 6, Article 831 (2023). Published: October 28, 2023. PDF , DOI: 10.1007/s42979-023-02292-0

Book sections

  • (2021) Kangsoo Jung, Sayan Biswas, Catuscia Palamidessi: “Establishing the Price of Privacy in Federated Data Trading”. Protocols, Strands, and Logic, pp 232-250, LNCS 13066, Springer. Published: November 19, 2021. PDF , DOI: 10.1007/978-3-030-91631-2_13

Non-archival workshops

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