A new algorithm for training sparse autoencoders. - Ali Shahin Shamsabadi; Massoud Babaie-Zadeh; Seyyede Zohreh Seyyedsalehi; Hamid R. Rabiee; Christian Jutten (EUSIPCO, 2017)

A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics. - Seyed Ali Ossia; Ali Shahin Shamsabadi; Ali Taheri; Hamid R. Rabiee; Nicholas D. Lane; Hamed Haddadi (CoRR, 2017)

Privacy-Preserving Deep Inference for Rich User Data on The Cloud. - Seyed Ali Ossia; Ali Shahin Shamsabadi; Ali Taheri; Kleomenis Katevas; Hamid R. Rabiee; Nicholas D. Lane; Hamed Haddadi (CoRR, 2017)

Distributed One-Class Learning. - Ali Shahin Shamsabadi; Hamed Haddadi; Andrea Cavallaro (ICIP, 2018)

Providing Occupancy as a Service with Databox. - Poonam Yadav; John Moore; Qi Li; Richard Mortier; Anthony Brown 001; Andy Crabtree; Chris Greenhalgh; Derek McAuley; Yousef Amar; Ali Shahin Shamsabadi; Hamed Haddadi (CitiFog@SenSys, 2018)

Private and Scalable Personal Data Analytics Using Hybrid Edge-to-Cloud Deep Learning. - Seyed Ali Ossia; Ali Shahin Shamsabadi; Ali Taheri; Hamid R. Rabiee; Hamed Haddadi (Computer, 2018)

QUOTIENT: Two-Party Secure Neural Network Training and Prediction. - Nitin Agrawal 002; Ali Shahin Shamsabadi; Matt J. Kusner; Adrià Gascón (CCS, 2019)

Poster: Towards Characterizing and Limiting Information Exposure in DNN Layers. - Fan Mo 004; Ali Shahin Shamsabadi; Kleomenis Katevas; Andrea Cavallaro; Hamed Haddadi (CCS, 2019)

Scene Privacy Protection. - Chau Yi Li; Ali Shahin Shamsabadi; Ricardo Sanchez-Matilla; Riccardo Mazzon; Andrea Cavallaro (ICASSP, 2019)

Towards Characterizing and Limiting Information Exposure in DNN Layers. - Fan Mo 004; Ali Shahin Shamsabadi; Kleomenis Katevas; Andrea Cavallaro; Hamed Haddadi (CoRR, 2019)

ColorFool: Semantic Adversarial Colorization. - Ali Shahin Shamsabadi; Ricardo Sánchez-Matilla; Andrea Cavallaro (CVPR, 2020)

Edgefool: an Adversarial Image Enhancement Filter. - Ali Shahin Shamsabadi; Changjae Oh; Andrea Cavallaro (ICASSP, 2020)

Deep Learning for Privacy in Multimedia. - Andrea Cavallaro; Mohammad Malekzadeh; Ali Shahin Shamsabadi (ACM Multimedia, 2020)

DarkneTZ: towards model privacy at the edge using trusted execution environments. - Fan Mo 004; Ali Shahin Shamsabadi; Kleomenis Katevas; Soteris Demetriou; Ilias Leontiadis; Andrea Cavallaro; Hamed Haddadi (MobiSys, 2020)

A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics. - Seyed Ali Osia; Ali Shahin Shamsabadi; Sina Sajadmanesh; Ali Taheri; Kleomenis Katevas; Hamid R. Rabiee; Nicholas D. Lane; Hamed Haddadi (IEEE Internet Things J., 2020)

PrivEdge: From Local to Distributed Private Training and Prediction. - Ali Shahin Shamsabadi; Adrià Gascón; Hamed Haddadi; Andrea Cavallaro (IEEE Trans. Inf. Forensics Secur., 2020)

Deep Private-Feature Extraction. - Seyed Ali Osia; Ali Taheri; Ali Shahin Shamsabadi; Kleomenis Katevas; Hamed Haddadi; Hamid R. Rabiee (IEEE Trans. Knowl. Data Eng., 2020)

Exploiting Vulnerabilities of Deep Neural Networks for Privacy Protection. - Ricardo Sanchez-Matilla; Chau Yi Li; Ali Shahin Shamsabadi; Riccardo Mazzon; Andrea Cavallaro (IEEE Trans. Multim., 2020)

FoolHD: Fooling Speaker Identification by Highly Imperceptible Adversarial Disturbances. - Ali Shahin Shamsabadi; Francisco Sepúlveda Teixeira; Alberto Abad; Bhiksha Raj; Andrea Cavallaro; Isabel Trancoso (ICASSP, 2021)

On the Reversibility of Adversarial Attacks. - Chau Yi Li; Ricardo Sánchez-Matilla; Ali Shahin Shamsabadi; Riccardo Mazzon; Andrea Cavallaro (ICIP, 2021)

Semantically Adversarial Learnable Filters. - Ali Shahin Shamsabadi; Changjae Oh; Andrea Cavallaro (IEEE Trans. Image Process., 2021)

A Zest of LIME: Towards Architecture-Independent Model Distances. - Hengrui Jia 001; Hongyu Chen; Jonas Guan; Ali Shahin Shamsabadi; Nicolas Papernot (ICLR, 2022)

Washing The Unwashable : On The (Im)possibility of Fairwashing Detection. - Ali Shahin Shamsabadi; Mohammad Yaghini; Natalie Dullerud; Sierra Calanda Wyllie; Ulrich Aïvodji; Aisha Alaagib; Sébastien Gambs; Nicolas Papernot (NeurIPS, 2022)

Pipe Overflow: Smashing Voice Authentication for Fun and Profit. - Shimaa Ahmed; Yash Wani; Ali Shahin Shamsabadi; Mohammad Yaghini; Ilia Shumailov; Nicolas Papernot; Kassem Fawaz (CoRR, 2022)

When the Curious Abandon Honesty: Federated Learning Is Not Private. - Franziska Boenisch; Adam Dziedzic; Roei Schuster; Ali Shahin Shamsabadi; Ilia Shumailov; Nicolas Papernot (EuroS&P, 2023)

Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation. - Franziska Boenisch; Adam Dziedzic; Roei Schuster; Ali Shahin Shamsabadi; Ilia Shumailov; Nicolas Papernot (EuroS&P, 2023)

Confidential-PROFITT: Confidential PROof of FaIr Training of Trees. - Ali Shahin Shamsabadi; Sierra Calanda Wyllie; Nicholas Franzese; Natalie Dullerud; Sébastien Gambs; Nicolas Papernot; Xiao Wang 012; Adrian Weller (ICLR, 2023)

Mnemonist: Locating Model Parameters that Memorize Training Examples. - Ali Shahin Shamsabadi; Jamie Hayes; Borja Balle; Adrian Weller (UAI, 2023)

Tubes Among Us: Analog Attack on Automatic Speaker Identification. - Shimaa Ahmed; Yash Wani; Ali Shahin Shamsabadi; Mohammad Yaghini; Ilia Shumailov; Nicolas Papernot; Kassem Fawaz (USENIX Security Symposium, 2023)

GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation. - Sina Sajadmanesh; Ali Shahin Shamsabadi; Aurélien Bellet; Daniel Gatica-Perez (USENIX Security Symposium, 2023)

Is Federated Learning a Practical PET Yet? - Franziska Boenisch; Adam Dziedzic; Roei Schuster; Ali Shahin Shamsabadi; Ilia Shumailov; Nicolas Papernot (CoRR, 2023)

Identifying and Mitigating Privacy Risks Stemming from Language Models: A Survey. - Victoria Smith; Ali Shahin Shamsabadi; Carolyn Ashurst; Adrian Weller (CoRR, 2023)

Private Multi-Winner Voting for Machine Learning. - Adam Dziedzic; Christopher A. Choquette-Choo; Natalie Dullerud; Vinith M. Suriyakumar; Ali Shahin Shamsabadi; Muhammad Ahmad Kaleem; Somesh Jha; Nicolas Papernot; Xiao Wang 012 (Proc. Priv. Enhancing Technol., 2023)

Differentially Private Speaker Anonymization. - Ali Shahin Shamsabadi; Brij Mohan Lal Srivastava; Aurélien Bellet; Nathalie Vauquier; Emmanuel Vincent 001; Mohamed Maouche; Marc Tommasi; Nicolas Papernot (Proc. Priv. Enhancing Technol., 2023)

Losing Less: A Loss for Differentially Private Deep Learning. - Ali Shahin Shamsabadi; Nicolas Papernot (Proc. Priv. Enhancing Technol., 2023)

Confidential-DPproof: Confidential Proof of Differentially Private Training. - Ali Shahin Shamsabadi; Gefei Tan; Tudor Cebere; Aurélien Bellet; Hamed Haddadi; Nicolas Papernot; Xiao Wang 012; Adrian Weller (ICLR, 2024)

Nebula: Efficient, Private and Accurate Histogram Estimation. - Ali Shahin Shamsabadi; Peter Snyder; Ralph Giles; Aurélien Bellet; Hamed Haddadi (CoRR, 2024)

Context-Aware Membership Inference Attacks against Pre-trained Large Language Models. - Hongyan Chang; Ali Shahin Shamsabadi; Kleomenis Katevas; Hamed Haddadi; Reza Shokri (CoRR, 2024)

OATH: Efficient and Flexible Zero-Knowledge Proofs of End-to-End ML Fairness. - Olive Franzese; Ali Shahin Shamsabadi; Hamed Haddadi (CoRR, 2024)

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation. - Rob Romijnders; Stefanos Laskaridis; Ali Shahin Shamsabadi; Hamed Haddadi (CoRR, 2025)

Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention. - Stephan Rabanser; Ali Shahin Shamsabadi; Olive Franzese; Xiao Wang 012; Adrian Weller; Nicolas Papernot (CoRR, 2025)

Membership and Memorization in LLM Knowledge Distillation. - Ziqi Zhang; Ali Shahin Shamsabadi; Hanxiao Lu; Yifeng Cai; Hamed Haddadi (CoRR, 2025)