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Umeå University
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Saloni Kwatra

Contact

E-mail
saloni.kwatra@umu.se
Phone

Works at

Affiliation
Doctoral student at Department of Computing Science
Location
MIT-huset, Umeå universitet, MIT.A.420 Umeå universitet, 901 87 Umeå
Navigating data privacy and utility: a strategic perspective
UMINF, 24.10
Kwatra, Saloni
Balancing act: navigating the privacy-utility spectrum in principal component analysis
Proceedings of the International Conference on Security and Cryptography, Science and Technology Publications, Lda 2024 : 850-857
Kwatra, Saloni; Monreale, Anna; Naretto, Francesca
Empirical evaluation of synthetic data created by generative models via attribute inference attack
Privacy and identity management: sharing in a digital world, Springer 2024 : 282-291
Kwatra, Saloni; Torra, Vicenç
DISCOLEAF: Personalized DIScretization of COntinuous Attributes for LEArning with Federated Decision Trees
Privacy in statistical databases: International Conference, PSD 2024, Antibes Juan-les-Pins, France, September 25–27, 2024, Proceedings, Springer Nature 2024 : 344-357
Kwatra, Saloni; Torra, Vicenç
Integrally private model selection for support vector machine
Computer Security. ESORICS 2023 International Workshops: CyberICS, DPM, CBT, and SECPRE, The Hague, The Netherlands, September 25–29, 2023, Revised Selected Papers, Part I, Springer Nature 2024 : 249-259
Kwatra, Saloni; Varshney, Ayush K.; Torra, Vicenç
Data reconstruction attack against principal component analysis
Security and Privacy in Social Networks and Big Data: 9th International Symposium, SocialSec 2023, Canterbury, UK, August 14–16, 2023, Springer Science+Business Media B.V. 2023 : 79-92
Kwatra, Saloni; Torra, Vicenç
A k-Anonymised Federated Learning Framework with Decision Trees
Data Privacy Management, Cryptocurrencies and Blockchain Technology, Springer Science+Business Media B.V. 2022 : 106-120
Kwatra, Saloni; Torra, Vicenç
A Survey on Tree Aggregation
2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Kwatra, Saloni; Torra, Vicenç
Rakshit: attacking and defending federated learning with decision trees
Kwatra, Saloni; Torra, Vicenç
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