311 citations · 675 across the 6 of their papers we have counts for
6 papers
Information Leakage in Embedding Models
Congzheng Song, Ananth Raghunathan
Embeddings are functions that map raw input data to low-dimensional vector representations, while preserving important semantic information about the inputs. Pre-training embedding…
Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov +4
Recently, a number of approaches and techniques have been introduced for reporting software statistics with strong privacy guarantees. These range from abstract algorithms to compr…
That which we call private
Úlfar Erlingsson, Ilya Mironov, Ananth Raghunathan +1
The guarantees of security and privacy defenses are often strengthened by relaxing the assumptions made about attackers or the context in which defenses are deployed. Such relaxati…
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov +3
Sensitive statistics are often collected across sets of users, with repeated collection of reports done over time. For example, trends in users' private preferences or software usa…
Scalable Private Learning with PATE
Nicolas Papernot, Shuang Song, Ilya Mironov +3
The rapid adoption of machine learning has increased concerns about the privacy implications of machine learning models trained on sensitive data, such as medical records or other…
Prochlo: Strong Privacy for Analytics in the Crowd
Andrea Bittau, Úlfar Erlingsson, Petros Maniatis +7
The large-scale monitoring of computer users' software activities has become commonplace, e.g., for application telemetry, error reporting, or demographic profiling. This paper des…