Ali Shahin Shamsabadi
Ali Shahin Shamsabadi
Privacy Researcher at Brave Software
Verified email at - Homepage
Cited by
Cited by
A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics
SA Osia, AS Shamsabadi, A Taheri, HR Rabiee, N Lane, H Haddadi
IEEE Internet of Things Journal, 2020
QUOTIENT: Two-Party Secure Neural Network Training and Prediction
N Agrawal, AS Shamsabadi, MJ Kusner, A Gascón
26th ACM Conference on Computer and Communications Security (CCS), 2019, 2019
DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments
F Mo, AS Shamsabadi, K Katevas, S Demetriou, I Leontiadis, A Cavallaro, ...
ACM International Conference on Mobile Systems, Applications, and Services …, 2020
ColorFool: Semantic Adversarial Colorization
AS Shamsabadi, R Sanchez-Matilla, A Cavallaro
Conference on Computer Vision and Pattern Recognition (CVPR), 2020
Deep Private-Feature Extraction
SA Osia, A Taheri, AS Shamsabadi, K Katevas, H Haddadi, HR Rabiee
IEEE Transactions on Knowledge and Data Engineering, 2018
When the curious abandon honesty: Federated learning is not private
F Boenisch, A Dziedzic, R Schuster, AS Shamsabadi, I Shumailov, ...
2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P), 175-199, 2023
Private and scalable personal data analytics using hybrid edge-to-cloud deep learning
SA Osia, AS Shamsabadi, A Taheri, HR Rabiee, H Haddadi
Computer 51 (5), 42-49, 2018
GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation
S Sajadmanesh, AS Shamsabadi, A Bellet, D Gatica-Perez
32nd USENIX Security Symposium, 2022
FoolHD: Fooling speaker identification by Highly imperceptible adversarial Disturbances
AS Shamsabadi, FS Teixeira, A Abad, B Raj, A Cavallaro, I Trancoso
46th IEEE International Conference on Acoustics, Speech, and Signal …, 2021
Exploiting vulnerabilities of deep neural networks for privacy protection
R Sanchez-Matilla, CY Li, AS Shamsabadi, R Mazzon, A Cavallaro
IEEE Transactions on Multimedia 22 (7), 1862-1873, 2020
PrivEdge: From Local to Distributed Private Training and Prediction
AS Shamsabadi, A Gascon, H Haddadi, A Cavallaro
IEEE Transactions on Information Forensics and Security (TIFS), 2020
EdgeFool: An Adversarial Image Enhancement Filter
AS Shamsabadi, C Oh, A Cavallaro
45th IEEE International Conference on Acoustics, Speech, and Signal …, 2020
Scene privacy protection
CY Li, AS Shamsabadi, R Sanchez-Matilla, R Mazzon, A Cavallaro
44th IEEE International Conference on Acoustics, Speech and Signal …, 2019
Differentially private speaker anonymization
AS Shamsabadi, BML Srivastava, A Bellet, N Vauquier, E Vincent, ...
Privacy Enhancing Technologies Symposium, 2022
A zest of lime: Towards architecture-independent model distances
H Jia, H Chen, J Guan, AS Shamsabadi, N Papernot
International Conference on Learning Representations, 2021
Privacy-preserving deep inference for rich user data on the cloud
SA Osia, AS Shamsabadi, A Taheri, K Katevas, HR Rabiee, ND Lane, ...
arXiv preprint arXiv:1710.01727, 2017
Identifying and mitigating privacy risks stemming from language models: A survey
V Smith, AS Shamsabadi, C Ashurst, A Weller
arXiv preprint arXiv:2310.01424, 2023
A new algorithm for training sparse autoencoders
AS Shamsabadi, M Babaie-Zadeh, SZ Seyyedsalehi, HR Rabiee, ...
25th European Signal Processing Conference (EUSIPCO), 2141-2145, 2017
Confidential-PROFITT: Confidential PROof of FaIr Training of Trees
AS Shamsabadi, SC Wyllie, N Franzese, N Dullerud, S Gambs, ...
International Conference on Learning Representations, 2023
Washing The Unwashable: On The (Im) possibility of Fairwashing Detection
AS Shamsabadi, M Yaghini, N Dullerud, S Wyllie, U Aïvodji, AAA Mkean, ...
Advances in Neural Information Processing Systems, 2022
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