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ORCIDAli Shahin Shamsabadi
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2020 – today
- 2024
[c21]Ali Shahin Shamsabadi, Gefei Tan, Tudor Cebere, Aurélien Bellet, Hamed Haddadi, Nicolas Papernot, Xiao Wang, Adrian Weller:
Confidential-DPproof: Confidential Proof of Differentially Private Training. ICLR 2024- [i24]Ali Shahin Shamsabadi, Peter Snyder, Ralph Giles, Aurélien Bellet, Hamed Haddadi:
Nebula: Efficient, Private and Accurate Histogram Estimation. CoRR abs/2409.09676 (2024) - [i23]Hongyan Chang, Ali Shahin Shamsabadi, Kleomenis Katevas, Hamed Haddadi, Reza Shokri:
Context-Aware Membership Inference Attacks against Pre-trained Large Language Models. CoRR abs/2409.13745 (2024) - [i22]Olive Franzese, Ali Shahin Shamsabadi, Hamed Haddadi:
OATH: Efficient and Flexible Zero-Knowledge Proofs of End-to-End ML Fairness. CoRR abs/2410.02777 (2024) - 2023
- [j9]Ali Shahin Shamsabadi, Brij Mohan Lal Srivastava, Aurélien Bellet, Nathalie Vauquier, Emmanuel Vincent, Mohamed Maouche, Marc Tommasi, Nicolas Papernot:
Differentially Private Speaker Anonymization. Proc. Priv. Enhancing Technol. 2023(1): 98-114 (2023) - [j8]Adam Dziedzic, Christopher A. Choquette-Choo, Natalie Dullerud, Vinith M. Suriyakumar, Ali Shahin Shamsabadi, Muhammad Ahmad Kaleem, Somesh Jha, Nicolas Papernot, Xiao Wang:
Private Multi-Winner Voting for Machine Learning. Proc. Priv. Enhancing Technol. 2023(1): 527-555 (2023) - [j7]Ali Shahin Shamsabadi, Nicolas Papernot:
Losing Less: A Loss for Differentially Private Deep Learning. Proc. Priv. Enhancing Technol. 2023(3): 307-320 (2023) - [c20]Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot:
When the Curious Abandon Honesty: Federated Learning Is Not Private. EuroS&P 2023: 175-199 - [c19]Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot:
Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation. EuroS&P 2023: 241-257 - [c18]Ali Shahin Shamsabadi, Sierra Calanda Wyllie, Nicholas Franzese, Natalie Dullerud, Sébastien Gambs, Nicolas Papernot, Xiao Wang, Adrian Weller:
Confidential-PROFITT: Confidential PROof of FaIr Training of Trees. ICLR 2023 - [c17]Ali Shahin Shamsabadi, Jamie Hayes, Borja Balle, Adrian Weller:
Mnemonist: Locating Model Parameters that Memorize Training Examples. UAI 2023: 1879-1888 - [c16]Shimaa Ahmed, Yash Wani, Ali Shahin Shamsabadi, Mohammad Yaghini, Ilia Shumailov, Nicolas Papernot, Kassem Fawaz:
Tubes Among Us: Analog Attack on Automatic Speaker Identification. USENIX Security Symposium 2023: 265-282 - [c15]Sina Sajadmanesh, Ali Shahin Shamsabadi, Aurélien Bellet, Daniel Gatica-Perez:
GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation. USENIX Security Symposium 2023: 3223-3240 - [i21]Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot:
Is Federated Learning a Practical PET Yet? CoRR abs/2301.04017 (2023) - [i20]Victoria Smith, Ali Shahin Shamsabadi, Carolyn Ashurst, Adrian Weller:
Identifying and Mitigating Privacy Risks Stemming from Language Models: A Survey. CoRR abs/2310.01424 (2023) - 2022
- [c14]Hengrui Jia, Hongyu Chen, Jonas Guan, Ali Shahin Shamsabadi, Nicolas Papernot:
A Zest of LIME: Towards Architecture-Independent Model Distances. ICLR 2022 - [c13]Ali Shahin Shamsabadi, Mohammad Yaghini, Natalie Dullerud, Sierra Calanda Wyllie, Ulrich Aïvodji, Aisha Alaagib, Sébastien Gambs, Nicolas Papernot:
Washing The Unwashable : On The (Im)possibility of Fairwashing Detection. NeurIPS 2022 - [i19]Shimaa Ahmed, Yash Wani, Ali Shahin Shamsabadi, Mohammad Yaghini, Ilia Shumailov, Nicolas Papernot, Kassem Fawaz:
Pipe Overflow: Smashing Voice Authentication for Fun and Profit. CoRR abs/2202.02751 (2022) - [i18]Ali Shahin Shamsabadi, Brij Mohan Lal Srivastava, Aurélien Bellet, Nathalie Vauquier, Emmanuel Vincent, Mohamed Maouche, Marc Tommasi, Nicolas Papernot:
Differentially Private Speaker Anonymization. CoRR abs/2202.11823 (2022) - [i17]Sina Sajadmanesh, Ali Shahin Shamsabadi, Aurélien Bellet, Daniel Gatica-Perez:
GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation. CoRR abs/2203.00949 (2022) - [i16]Chau Yi Li, Ricardo Sánchez-Matilla, Ali Shahin Shamsabadi, Riccardo Mazzon, Andrea Cavallaro:
On the reversibility of adversarial attacks. CoRR abs/2206.00772 (2022) - [i15]Adam Dziedzic, Christopher A. Choquette-Choo, Natalie Dullerud, Vinith Menon Suriyakumar, Ali Shahin Shamsabadi, Muhammad Ahmad Kaleem, Somesh Jha, Nicolas Papernot, Xiao Wang:
Private Multi-Winner Voting for Machine Learning. CoRR abs/2211.15410 (2022) - 2021
- [j6]Ali Shahin Shamsabadi
, Changjae Oh, Andrea Cavallaro:
Semantically Adversarial Learnable Filters. IEEE Trans. Image Process. 30: 8075-8087 (2021) - [c12]Ali Shahin Shamsabadi, Francisco Sepúlveda Teixeira, Alberto Abad, Bhiksha Raj, Andrea Cavallaro, Isabel Trancoso:
FoolHD: Fooling Speaker Identification by Highly Imperceptible Adversarial Disturbances. ICASSP 2021: 6159-6163 - [c11]Chau Yi Li, Ricardo Sánchez-Matilla, Ali Shahin Shamsabadi, Riccardo Mazzon, Andrea Cavallaro:
On the Reversibility of Adversarial Attacks. ICIP 2021: 3073-3077 - [i14]Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot:
When the Curious Abandon Honesty: Federated Learning Is Not Private. CoRR abs/2112.02918 (2021) - 2020
- [j5]Seyed Ali Osia, Ali Shahin Shamsabadi, Sina Sajadmanesh, Ali Taheri, Kleomenis Katevas, Hamid R. Rabiee, Nicholas D. Lane, Hamed Haddadi:
A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics. IEEE Internet Things J. 7(5): 4505-4518 (2020) - [j4]Ali Shahin Shamsabadi, Adrià Gascón, Hamed Haddadi, Andrea Cavallaro:
PrivEdge: From Local to Distributed Private Training and Prediction. IEEE Trans. Inf. Forensics Secur. 15: 3819-3831 (2020) - [j3]Seyed Ali Osia, Ali Taheri, Ali Shahin Shamsabadi, Kleomenis Katevas, Hamed Haddadi, Hamid R. Rabiee:
Deep Private-Feature Extraction. IEEE Trans. Knowl. Data Eng. 32(1): 54-66 (2020) - [j2]Ricardo Sanchez-Matilla, Chau Yi Li, Ali Shahin Shamsabadi, Riccardo Mazzon, Andrea Cavallaro:
Exploiting Vulnerabilities of Deep Neural Networks for Privacy Protection. IEEE Trans. Multim. 22(7): 1862-1873 (2020) - [c10]Ali Shahin Shamsabadi, Ricardo Sánchez-Matilla, Andrea Cavallaro:
ColorFool: Semantic Adversarial Colorization. CVPR 2020: 1148-1157 - [c9]Ali Shahin Shamsabadi, Changjae Oh, Andrea Cavallaro:
Edgefool: an Adversarial Image Enhancement Filter. ICASSP 2020: 1898-1902 - [c8]Andrea Cavallaro, Mohammad Malekzadeh, Ali Shahin Shamsabadi:
Deep Learning for Privacy in Multimedia. ACM Multimedia 2020: 4777-4778 - [c7]Fan Mo, Ali Shahin Shamsabadi, Kleomenis Katevas, Soteris Demetriou, Ilias Leontiadis, Andrea Cavallaro, Hamed Haddadi:
DarkneTZ: towards model privacy at the edge using trusted execution environments. MobiSys 2020: 161-174 - [i13]Ali Shahin Shamsabadi, Adrià Gascón, Hamed Haddadi, Andrea Cavallaro:
PrivEdge: From Local to Distributed Private Training and Prediction. CoRR abs/2004.05574 (2020) - [i12]Fan Mo, Ali Shahin Shamsabadi, Kleomenis Katevas, Soteris Demetriou, Ilias Leontiadis, Andrea Cavallaro, Hamed Haddadi:
DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments. CoRR abs/2004.05703 (2020) - [i11]Ricardo Sanchez-Matilla, Chau Yi Li, Ali Shahin Shamsabadi, Riccardo Mazzon, Andrea Cavallaro:
Exploiting vulnerabilities of deep neural networks for privacy protection. CoRR abs/2007.09766 (2020) - [i10]Ali Shahin Shamsabadi, Changjae Oh, Andrea Cavallaro:
Semantically Adversarial Learnable Filters. CoRR abs/2008.06069 (2020) - [i9]Ali Shahin Shamsabadi, Francisco Sepúlveda Teixeira, Alberto Abad, Bhiksha Raj, Andrea Cavallaro, Isabel Trancoso:
FoolHD: Fooling speaker identification by Highly imperceptible adversarial Disturbances. CoRR abs/2011.08483 (2020)
2010 – 2019
- 2019
- [c6]Nitin Agrawal, Ali Shahin Shamsabadi, Matt J. Kusner, Adrià Gascón:
QUOTIENT: Two-Party Secure Neural Network Training and Prediction. CCS 2019: 1231-1247 - [c5]Fan Mo, Ali Shahin Shamsabadi, Kleomenis Katevas, Andrea Cavallaro, Hamed Haddadi:
Poster: Towards Characterizing and Limiting Information Exposure in DNN Layers. CCS 2019: 2653-2655 - [c4]Chau Yi Li, Ali Shahin Shamsabadi, Ricardo Sanchez-Matilla, Riccardo Mazzon, Andrea Cavallaro:
Scene Privacy Protection. ICASSP 2019: 2502-2506 - [i8]Nitin Agrawal, Ali Shahin Shamsabadi, Matt J. Kusner, Adrià Gascón:
QUOTIENT: Two-Party Secure Neural Network Training and Prediction. CoRR abs/1907.03372 (2019) - [i7]Fan Mo, Ali Shahin Shamsabadi, Kleomenis Katevas, Andrea Cavallaro, Hamed Haddadi:
Towards Characterizing and Limiting Information Exposure in DNN Layers. CoRR abs/1907.06034 (2019) - [i6]Ali Shahin Shamsabadi, Changjae Oh, Andrea Cavallaro:
EdgeFool: An Adversarial Image Enhancement Filter. CoRR abs/1910.12227 (2019) - [i5]Ali Shahin Shamsabadi, Ricardo Sanchez-Matilla, Andrea Cavallaro:
ColorFool: Semantic Adversarial Colorization. CoRR abs/1911.10891 (2019) - 2018
- [j1]Seyed Ali Ossia, Ali Shahin Shamsabadi, Ali Taheri, Hamid R. Rabiee, Hamed Haddadi:
Private and Scalable Personal Data Analytics Using Hybrid Edge-to-Cloud Deep Learning. Computer 51(5): 42-49 (2018) - [c3]Ali Shahin Shamsabadi, Hamed Haddadi, Andrea Cavallaro:
Distributed One-Class Learning. ICIP 2018: 4123-4127 - [c2]Poonam Yadav, John Moore, Qi Li, Richard Mortier, Anthony Brown, Andy Crabtree, Chris Greenhalgh, Derek McAuley, Yousef Amar, Ali Shahin Shamsabadi, Hamed Haddadi:
Providing Occupancy as a Service with Databox. CitiFog@SenSys 2018: 29-34 - [i4]Seyed Ali Ossia, Ali Taheri, Ali Shahin Shamsabadi, Kleomenis Katevas, Hamed Haddadi, Hamid R. Rabiee:
Deep Private-Feature Extraction. CoRR abs/1802.03151 (2018) - [i3]Ali Shahin Shamsabadi, Hamed Haddadi, Andrea Cavallaro:
Distributed One-class Learning. CoRR abs/1802.03583 (2018) - 2017
- [c1]Ali Shahin Shamsabadi, Massoud Babaie-Zadeh, Seyyede Zohreh Seyyedsalehi, Hamid R. Rabiee, Christian Jutten:
A new algorithm for training sparse autoencoders. EUSIPCO 2017: 2141-2145 - [i2]Seyed Ali Ossia, Ali Shahin Shamsabadi, Ali Taheri, Hamid R. Rabiee, Nicholas D. Lane, Hamed Haddadi:
A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics. CoRR abs/1703.02952 (2017) - [i1]Seyed Ali Ossia, Ali Shahin Shamsabadi, Ali Taheri, Kleomenis Katevas, Hamid R. Rabiee, Nicholas D. Lane, Hamed Haddadi:
Privacy-Preserving Deep Inference for Rich User Data on The Cloud. CoRR abs/1710.01727 (2017)
Coauthor Index
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last updated on 2024-12-04 21:10 CET by the dblp team
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