I
Ilya Mironov
Researcher at Google
Publications - 95
Citations - 14343
Ilya Mironov is an academic researcher from Google. The author has contributed to research in topics: Differential privacy & Computer science. The author has an hindex of 36, co-authored 90 publications receiving 9799 citations. Previous affiliations of Ilya Mironov include Stanford University & Microsoft.
Papers
More filters
Proceedings ArticleDOI
Deep Learning with Differential Privacy
TL;DR: In this paper, the authors develop new algorithmic techniques for learning and a refined analysis of privacy costs within the framework of differential privacy, and demonstrate that they can train deep neural networks with nonconvex objectives, under a modest privacy budget, and at a manageable cost in software complexity, training efficiency, and model quality.
Proceedings ArticleDOI
Deep Learning with Differential Privacy
TL;DR: This work develops new algorithmic techniques for learning and a refined analysis of privacy costs within the framework of differential privacy, and demonstrates that deep neural networks can be trained with non-convex objectives, under a modest privacy budget, and at a manageable cost in software complexity, training efficiency, and model quality.
Book ChapterDOI
Our data, ourselves: privacy via distributed noise generation
TL;DR: In this paper, a distributed protocol for generating shares of random noise, secure against malicious participants, was proposed, where the purpose of the noise generation is to create a distributed implementation of the privacy-preserving statistical databases described in recent papers.
Proceedings ArticleDOI
Rényi Differential Privacy
TL;DR: This work argues that the useful analytical tool can be used as a privacy definition, compactly and accurately representing guarantees on the tails of the privacy loss, and demonstrates that the new definition shares many important properties with the standard definition of differential privacy.
Proceedings ArticleDOI
Differentially private recommender systems: Building privacy into the Netflix Prize contenders
Frank McSherry,Ilya Mironov +1 more
TL;DR: This work considers the problem of producing recommendations from collective user behavior while simultaneously providing guarantees of privacy for these users, and finds that several of the leading approaches in the Netflix Prize competition can be adapted to provide differential privacy, without significantly degrading their accuracy.