6.3k citations · 8.5k across the 15 of their papers we have counts for
16 papers
Wide Network Learning with Differential Privacy
Huanyu Zhang, Ilya Mironov, Meisam Hejazinia
Despite intense interest and considerable effort, the current generation of neural networks suffers a significant loss of accuracy under most practically relevant privacy training…
Cryptanalytic Extraction of Neural Network Models
Nicholas Carlini, Matthew Jagielski, Ilya Mironov
We argue that the machine learning problem of model extraction is actually a cryptanalytic problem in disguise, and should be studied as such. Given oracle access to a neural netwo…
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…
Rényi Differential Privacy of the Sampled Gaussian Mechanism
Ilya Mironov, Kunal Talwar, Li Zhang
The Sampled Gaussian Mechanism (SGM)---a composition of subsampling and the additive Gaussian noise---has been successfully used in a number of machine learning applications. The m…
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…