Truth Serum
Florian Tramèr, Reza Shokri, Ayrton San Joaquin, Hoang Le, Matthew Jagielski, Sanghyun Hong, Nicholas Carlini
2022Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security58 citationsDOIOpen Access PDF
Abstract
We introduce a new class of attacks on machine learning models. We show that an adversary who can poison a training dataset can cause models trained on this dataset to leak significant private details of training points belonging to other parties. Our active inference attacks connect two independent lines of work targeting the integrity and privacy of machine learning training data.
Topics & Concepts
Computer scienceAdversaryInferenceClass (philosophy)Artificial intelligenceTraining setMachine learningComputer securityPrivacy-Preserving Technologies in DataAdversarial Robustness in Machine LearningCryptography and Data Security