Tag: privacy (23 references)
Exploring the limits of strong membership inference attacks on large language models
Machine Unlearning: A Survey
Comprehensive survey of machine unlearning covering definitions, scenarios, verification methods, and applications. Cited in the International AI Safety Report 2025 as a pioneering paradigm for removing sensitive information.
Quantifying Memorization Across Neural Language Models
Datamodels: Predicting Predictions from Training Data
Proposes datamodels that predict model outputs as a function of training data subsets, providing a framework for understanding data attribution through retraining experiments.
Machine Unlearning
Introduces SISA (Sharded, Isolated, Sliced, Aggregated) training for efficient exact machine unlearning. Partitions data into shards with separate models, enabling targeted retraining when data must be forgotten.
Extracting Training Data from Large Language Models
The Biggest Lie on the Internet: Ignoring the Privacy Policies and Terms of Service Policies of Social Networking Services
Are anonymity-seekers just like everybody else? An analysis of contributions to Wikipedia from Tor
The Secret Sharer: Measuring Unintended Memorization in Neural Networks
Privacy, anonymity, and perceived risk in open collaboration: A study of service providers
Rosenbach v. Six Flags Entertainment Corp.
The Dark (Patterns) Side of UX Design
Reality and Perception of Copyright Terms of Service for Online Content Creation
Towards Making Systems Forget with Machine Unlearning
First formal definition of machine unlearning. Proposes converting learning algorithms into summation form to enable efficient data removal without full retraining. Foundational work establishing the unlearning problem.