190 citations · 316 across the 24 of their papers we have counts for
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cs.CL2023★ 11 cited
Identifying and Mitigating Privacy Risks Stemming from Language Models: A Survey
Victoria Smith, Ali Shahin Shamsabadi, Carolyn Ashurst +1
Large Language Models (LLMs) have shown greatly enhanced performance in recent years, attributed to increased size and extensive training data. This advancement has led to widespre…
cs.CR2023
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:…