Zobrazeno 1 - 10
of 69
pro vyhledávání: '"Sundaram Muthu"'
Publikováno v:
IEEE Access, Vol 11, Pp 99289-99303 (2023)
The analysis of 3D motion information is the key to solve various computer vision tasks. Scene flow estimation tackles the problem of obtaining the 3D motion field. In this paper, we review the recent scene flow estimation papers with a focus on lear
Externí odkaz:
https://doaj.org/article/0335c7a3c7b641b299c688437749dc91
Differentially Private Stochastic Gradient Descent (DP-SGD) is a popular iterative algorithm used to train machine learning models while formally guaranteeing the privacy of users. However, the privacy analysis of DP-SGD makes the unrealistic assumpt
Externí odkaz:
http://arxiv.org/abs/2407.06496
Synthetic data created by differentially private (DP) generative models is increasingly used in real-world settings. In this context, PATE-GAN has emerged as a popular algorithm, combining Generative Adversarial Networks (GANs) with the private train
Externí odkaz:
http://arxiv.org/abs/2406.13985
This paper presents a nearly tight audit of the Differentially Private Stochastic Gradient Descent (DP-SGD) algorithm in the black-box model. Our auditing procedure empirically estimates the privacy leakage from DP-SGD using membership inference atta
Externí odkaz:
http://arxiv.org/abs/2405.14106
Differentially private synthetic data generation (DP-SDG) algorithms are used to release datasets that are structurally and statistically similar to sensitive data while providing formal bounds on the information they leak. However, bugs in algorithm
Externí odkaz:
http://arxiv.org/abs/2405.10994
Autor:
Gadotti, Andrea, Houssiau, Florimond, Annamalai, Meenatchi Sundaram Muthu Selva, de Montjoye, Yves-Alexandre
Publikováno v:
USENIX Security 22 (2022)
Behavioral data generated by users' devices, ranging from emoji use to pages visited, are collected at scale to improve apps and services. These data, however, contain fine-grained records and can reveal sensitive information about individual users.
Externí odkaz:
http://arxiv.org/abs/2304.07134
Publikováno v:
Published in the Proceedings of the 33rd USENIX Security Symposium (USENIX Security 2024), please cite accordingly
Recent advances in synthetic data generation (SDG) have been hailed as a solution to the difficult problem of sharing sensitive data while protecting privacy. SDG aims to learn statistical properties of real data in order to generate "artificial" dat
Externí odkaz:
http://arxiv.org/abs/2301.10053
Autor:
Jestine Paul, Meenatchi Sundaram Muthu Selva Annamalai, William Ming, Ahmad Al Badawi, Bharadwaj Veeravalli, Khin Mi Mi Aung
Publikováno v:
IEEE Access, Vol 9, Pp 132084-132096 (2021)
Deep learning models such as long short-term memory (LSTM) are valuable classifiers for time series data like hourly clinical statistics. However, access to health data is challenging due to privacy and legal issues. Homomorphic encryption (HE) offer
Externí odkaz:
https://doaj.org/article/0114f573f07f4d96a7f337479fc5fd6b
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