Varun Chandrasekaran
Varun Chandrasekaran
Microsoft Research, University of Illinois Urbana-Champaign
Verified email at - Homepage
Cited by
Cited by
Machine unlearning
L Bourtoule, V Chandrasekaran, CA Choquette-Choo, H Jia, A Travers, ...
2021 IEEE Symposium on Security and Privacy (SP), 141-159, 2021
Entangled watermarks as a defense against model extraction
H Jia, CA Choquette-Choo, V Chandrasekaran, N Papernot
30th USENIX Security Symposium (USENIX Security 21), 1937-1954, 2021
Exploring connections between active learning and model extraction
V Chandrasekaran, K Chaudhuri, I Giacomelli, S Jha, S Yan
29th USENIX Security Symposium (USENIX Security 20), 1309-1326, 2020
On the effectiveness of mitigating data poisoning attacks with gradient shaping
S Hong, V Chandrasekaran, Y Kaya, T Dumitraş, N Papernot
arXiv preprint arXiv:2002.11497, 2020
Face-off: Adversarial face obfuscation
V Chandrasekaran, C Gao, B Tang, K Fawaz, S Jha, S Banerjee
arXiv preprint arXiv:2003.08861, 2020
PowerCut and Obfuscator: An Exploration of the Design Space for Privacy-Preserving Interventions for Voice Assistants
V Chandrasekaran, S Banerjee, B Mutlu, K Fawaz
arXiv preprint arXiv:1812.00263, 2018
Analyzing and improving neural networks by generating semantic counterexamples through differentiable rendering
L Jain, V Chandrasekaran, U Jang, W Wu, A Lee, A Yan, S Chen, S Jha, ...
arXiv preprint arXiv:1910.00727, 2019
Proof-of-learning: Definitions and practice
H Jia, M Yaghini, CA Choquette-Choo, N Dullerud, A Thudi, ...
2021 IEEE Symposium on Security and Privacy (SP), 1039-1056, 2021
Traversing the quagmire that is privacy in your smart home
C Gao, V Chandrasekaran, K Fawaz, S Banerjee
Proceedings of the 2018 Workshop on IoT Security and Privacy, 22-28, 2018
A general framework for detecting anomalous inputs to DNN classifiers
J Raghuram, V Chandrasekaran, S Jha, S Banerjee
International Conference on Machine Learning, 8764-8775, 2021
A framework for analyzing spectrum characteristics in large spatio-temporal scales
Y Zeng, V Chandrasekaran, S Banerjee, D Giustiniano
The 25th Annual International Conference on Mobile Computing and Networking …, 2019
Unrolling sgd: Understanding factors influencing machine unlearning
A Thudi, G Deza, V Chandrasekaran, N Papernot
2022 IEEE 7th European Symposium on Security and Privacy (EuroS&P), 303-319, 2022
SoK: Machine learning governance
V Chandrasekaran, H Jia, A Thudi, A Travers, M Yaghini, N Papernot
arXiv preprint arXiv:2109.10870, 2021
Secure mobile identities
V Chandrasekaran, F Amjad, A Sharma, L Subramanian
arXiv preprint arXiv:1604.04667, 2016
Rearchitecting Classification Frameworks For Increased Robustness
V Chandrasekaran, B Tang, N Papernot, K Fawaz, S Jha, X Wu
arXiv preprint arXiv:1905.10900, 2019
On the Exploitability of Audio Machine Learning Pipelines to Surreptitious Adversarial Examples
A Travers, L Licollari, G Wang, V Chandrasekaran, A Dziedzic, D Lie, ...
arXiv preprint arXiv:2108.02010, 2021
Confidant: A privacy controller for social robots
B Tang, D Sullivan, B Cagiltay, V Chandrasekaran, K Fawaz, B Mutlu
arXiv preprint arXiv:2201.02712, 2022
Causally Constrained Data Synthesis for Private Data Release
V Chandrasekaran, D Edge, S Jha, A Sharma, C Zhang, S Tople
arXiv preprint arXiv:2105.13144, 2021
Method and Apparatus using Blended Biometric Data
V Chandrasekaran, R Chatterjee, X Fu, JY Cai, S Banerjee
US Patent App. 17/177,080, 2022
On the Fundamental Limits of Formally (Dis) Proving Robustness in Proof-of-Learning
C Fang, H Jia, A Thudi, M Yaghini, CA Choquette-Choo, N Dullerud, ...
arXiv preprint arXiv:2208.03567, 2022
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