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Sanjay Kariyappa
Sanjay Kariyappa
Sr AI Research Accociate, JP Morgan Chase
Verified email at jpmchase.com - Homepage
Title
Cited by
Cited by
Year
Improving adversarial robustness of ensembles with diversity training
S Kariyappa, MK Qureshi
arXiv preprint arXiv:1901.09981, 2019
932019
Maze: Data-free model stealing attack using zeroth-order gradient estimation
S Kariyappa, A Prakash, MK Qureshi
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern…, 2021
612021
Defending against model stealing attacks with adaptive misinformation
S Kariyappa, MK Qureshi
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern…, 2020
542020
Reducing the impact of phase-change memory conductance drift on the inference of large-scale hardware neural networks
S Ambrogio, M Gallot, K Spoon, H Tsai, C Mackin, M Wesson, ...
2019 IEEE International Electron Devices Meeting (IEDM), 6.1. 1-6.1. 4, 2019
492019
Enabling transparent memory-compression for commodity memory systems
V Young, S Kariyappa, MK Qureshi
2019 IEEE International Symposium on High Performance Computer Architecture…, 2019
302019
Noise-resilient DNN: tolerating noise in PCM-based AI accelerators via noise-aware training
S Kariyappa, H Tsai, K Spoon, S Ambrogio, P Narayanan, C Mackin, ...
IEEE Transactions on Electron Devices 68 (9), 4356-4362, 2021
172021
Protecting dnns from theft using an ensemble of diverse models
S Kariyappa, A Prakash, MK Qureshi
International Conference on Learning Representations, 2021
102021
Bespoke cache enclaves: Fine-grained and scalable isolation from cache side-channels via flexible set-partitioning
G Saileshwar, S Kariyappa, M Qureshi
2021 International Symposium on Secure and Private Execution Environment…, 2021
72021
ExPLoit: Extracting Private Labels in Split Learning
S Kariyappa, MK Qureshi
First IEEE Conference on Secure and Trustworthy Machine Learning, 2021
5*2021
Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning using Independent Component Analysis
S Kariyappa, C Guo, K Maeng, W Xiong, GE Suh, MK Qureshi, HHS Lee
arXiv preprint arXiv:2209.05578, 2022
22022
Enabling inference privacy with adaptive noise injection
S Kariyappa, O Dia, MK Qureshi
arXiv preprint arXiv:2104.02261, 2021
22021
Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information
K Maeng, C Guo, S Kariyappa, GE Suh
arXiv preprint arXiv:2305.04146, 2023
2023
Drift regularization to counteract variation in drift coefficients for analog accelerators
H Tsai, S Kariyappa
US Patent 11,514,326, 2022
2022
Understanding and Mitigating Privacy Vulnerabilities in Deep Learning
S Kariyappa
Georgia Institute of Technology, 2022
2022
Measuring and Controlling Split Layer Privacy Leakage Using Fisher Information
K Maeng, C Guo, S Kariyappa, E Suh
arXiv preprint arXiv:2209.10119, 2022
2022
Neural network accelerators resilient to conductance drift
H Tsai, S Ambrogio, S Kariyappa, M Gallot
US Patent App. 17/035,005, 2022
2022
2021 International Symposium on Secure and Private Execution Environment Design (SEED)| 978-1-6654-2025-9/21/$31.00 2021 IEEE| DOI: 10.1109/SEED51797. 2021.00034
A Aharon, I Akturk, FA Andargie, MA Arroyo, T Austin, A Awad, L Biernacki, ...
2021 International Symposium on Secure and Private Execution Environment Design (SEED)
G Saileshwar, S Kariyappa, M Qureshi
Semantics Preserving Adversarial Examples
S Kariyappa, O Dia
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Articles 1–19