68 citations · 162 across the 11 of their papers we have counts for
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cs.CR2023
The Adversarial Implications of Variable-Time Inference
Dudi Biton, Aditi Misra, Efrat Levy +6
Machine learning (ML) models are known to be vulnerable to a number of attacks that target the integrity of their predictions or the privacy of their training data. To carry out th…
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:…