173 citations · 449 across the 24 of their papers we have counts for
11 papers · 1 filter
Unmasking the Secret Code: Using Canaries to Show How Masked Context Controls Memorization in SFT
Avi Schwarzschild, Om Thakkar, Sahra Ghalebikesabi +2
Canaries, or synthetic training records containing known secrets, are widely used to assess whether language models reveal information from their training data. But how much do the…
Model Card for OpenAI Privacy Filter
Charles de Bourcy, Sahra Ghalebikesabi, Avi Schwarzschild +22
OpenAI Privacy Filter is a compact, bidirectional token-classification model for detecting and redacting personally identifiable information (PII) and secrets in unstructured text.…
Samplable Anonymous Aggregation for Private Federated Data Analysis
Kunal Talwar, Shan Wang, Audra McMillan +34
We revisit the problem of designing scalable protocols for private statistics and private federated learning when each device holds its private data. Locally differentially private…
Private Federated Statistics in an Interactive Setting
Audra McMillan, Omid Javidbakht, Kunal Talwar +21
Privately learning statistics of events on devices can enable improved user experience. Differentially private algorithms for such problems can benefit significantly from interacti…
Training a Tokenizer for Free with Private Federated Learning
Eugene Bagdasaryan, Congzheng Song, Rogier van Dalen +2
Federated learning with differential privacy, i.e. private federated learning (PFL), makes it possible to train models on private data distributed across users' devices without har…
You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion
Roei Schuster, Congzheng Song, Eran Tromer +1
Code autocompletion is an integral feature of modern code editors and IDEs. The latest generation of autocompleters uses neural language models, trained on public open-source code…
Auditing Data Provenance in Text-Generation Models
Congzheng Song, Vitaly Shmatikov
To help enforce data-protection regulations such as GDPR and detect unauthorized uses of personal data, we develop a new \emph{model auditing} technique that helps users check if t…
Exploiting Unintended Feature Leakage in Collaborative Learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro +1
Collaborative machine learning and related techniques such as federated learning allow multiple participants, each with his own training dataset, to build a joint model by training…
Chiron: Privacy-preserving Machine Learning as a Service
Tyler Hunt, Congzheng Song, Reza Shokri +2
Major cloud operators offer machine learning (ML) as a service, enabling customers who have the data but not ML expertise or infrastructure to train predictive models on this data.…
Machine Learning Models that Remember Too Much
Congzheng Song, Thomas Ristenpart, Vitaly Shmatikov
Machine learning (ML) is becoming a commodity. Numerous ML frameworks and services are available to data holders who are not ML experts but want to train predictive models on their…
Membership Inference Attacks against Machine Learning Models
Reza Shokri, Marco Stronati, Congzheng Song +1
We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained. We focus on the basic membership inference…