Faster differentially private convex optimization via second-order methods
Differentially private (stochastic) gradient descent is the workhorse of DP private machine
learning in both the convex and non-convex settings. Without privacy constraints, second …
learning in both the convex and non-convex settings. Without privacy constraints, second …
Composition of differential privacy & privacy amplification by subsampling
T Steinke - arXiv preprint arXiv:2210.00597, 2022 - arxiv.org
This chapter is meant to be part of the book" Differential Privacy for Artificial Intelligence
Applications." We give an introduction to the most important property of differential privacy …
Applications." We give an introduction to the most important property of differential privacy …
Optimal differentially private learning of thresholds and quasi-concave optimization
The problem of learning threshold functions is a fundamental one in machine learning.
Classical learning theory implies sample complexity of O (ξ− 1 log (1/β))(for generalization …
Classical learning theory implies sample complexity of O (ξ− 1 log (1/β))(for generalization …
Adaptive privacy composition for accuracy-first mechanisms
Although there has been work to develop ex-post private mechanisms from Ligett et al.'17
and Whitehouse et al'22 that seeks to provide privacy guarantees subject to a target level of …
and Whitehouse et al'22 that seeks to provide privacy guarantees subject to a target level of …
Control, confidentiality, and the right to be forgotten
Recent digital rights frameworks give users the right to delete their data from systems that
store and process their personal information (eg, the" right to be forgotten" in the GDPR) …
store and process their personal information (eg, the" right to be forgotten" in the GDPR) …
Advancing differential privacy: Where we are now and future directions for real-world deployment
In this article, we present a detailed review of current practices and state-of-the-art
methodologies in the field of differential privacy (DP), with a focus of advancing DP's …
methodologies in the field of differential privacy (DP), with a focus of advancing DP's …
Concentrated differential privacy for bandits
Bandits serve as the theoretical foundation of sequential learning and an algorithmic
foundation of modern recommender systems. However, recommender systems often rely on …
foundation of modern recommender systems. However, recommender systems often rely on …
Concurrent composition for interactive differential privacy with adaptive Privacy-Loss parameters
In this paper, we study the concurrent composition of interactive mechanisms with adaptively
chosen privacy-loss parameters. In this setting, the adversary can interleave queries to …
chosen privacy-loss parameters. In this setting, the adversary can interleave queries to …
Turbo: Effective Caching in Differentially-Private Databases
Differentially-private (DP) databases allow for privacy-preserving analytics over sensitive
datasets or data streams. In these systems, user privacy is a limited resource that must be …
datasets or data streams. In these systems, user privacy is a limited resource that must be …
Alistair: Efficient On-device Budgeting for Differentially-Private Ad-Measurement Systems
P Tholoniat, K Kostopoulou, P McNeely… - arXiv preprint arXiv …, 2024 - arxiv.org
With the impending removal of third-party cookies from major browsers and the introduction
of new privacy-preserving advertising APIs, the research community has a timely opportunity …
of new privacy-preserving advertising APIs, the research community has a timely opportunity …