190 citations · 316 across the 24 of their papers we have counts for
8 papers · 1 filter
Distributed and Private Textual Data Synthesis from Embeddings
Ergute Bao, Hongyan Chang, Ali Shahin Shamsabadi +2
We revisit differentially private (DP) text synthesis in the realistic setting of distributed users, where privacy concerns preclude a trusted curator with access to raw user texts…
Privacy Practices of Browser Agents
Alisha Ukani, Hamed Haddadi, Ali Shahin Shamsabadi +1
This paper presents a systematic evaluation of the privacy behaviors and attributes of eight recent, popular browser agents. Browser agents are software that automate Web browsing…
Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention
Stephan Rabanser, Ali Shahin Shamsabadi, Olive Franzese +3
Cautious predictions -- where a machine learning model abstains when uncertain -- are crucial for limiting harmful errors in safety-critical applications. In this work, we identify…
NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation
Rob Romijnders, Stefanos Laskaridis, Ali Shahin Shamsabadi +1
Large Language Models (LLM) are typically trained on vast amounts of data from various sources. Even when designed modularly (e.g., Mixture-of-Experts), LLMs can leak privacy on th…
Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation
Franziska Boenisch, Adam Dziedzic, Roei Schuster +3
Federated learning (FL) is a framework for users to jointly train a machine learning model. FL is promoted as a privacy-enhancing technology (PET) that provides data minimization:…
PrivEdge: From Local to Distributed Private Training and Prediction
Ali Shahin Shamsabadi, Adria Gascon, Hamed Haddadi +1
Machine Learning as a Service (MLaaS) operators provide model training and prediction on the cloud. MLaaS applications often rely on centralised collection and aggregation of user…
Towards Characterizing and Limiting Information Exposure in DNN Layers
Fan Mo, Ali Shahin Shamsabadi, Kleomenis Katevas +2
Pre-trained Deep Neural Network (DNN) models are increasingly used in smartphones and other user devices to enable prediction services, leading to potential disclosures of (sensiti…
QUOTIENT: Two-Party Secure Neural Network Training and Prediction
Nitin Agrawal, Ali Shahin Shamsabadi, Matt J. Kusner +1
Recently, there has been a wealth of effort devoted to the design of secure protocols for machine learning tasks. Much of this is aimed at enabling secure prediction from highly-ac…