Local differential privacy and its applications: A comprehensive survey
With the rapid development of low-cost consumer electronics and pervasive adoption of next
generation wireless communication technologies, a tremendous amount of data has been …
generation wireless communication technologies, a tremendous amount of data has been …
A critical review on the use (and misuse) of differential privacy in machine learning
We review the use of differential privacy (DP) for privacy protection in machine learning
(ML). We show that, driven by the aim of preserving the accuracy of the learned models, DP …
(ML). We show that, driven by the aim of preserving the accuracy of the learned models, DP …
Foundation models and fair use
Existing foundation models are trained on copyrighted material. Deploying these models
can pose both legal and ethical risks when data creators fail to receive appropriate …
can pose both legal and ethical risks when data creators fail to receive appropriate …
Advances and open problems in federated learning
Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …
devices or whole organizations) collaboratively train a model under the orchestration of a …
LDP-Fed: Federated learning with local differential privacy
This paper presents LDP-Fed, a novel federated learning system with a formal privacy
guarantee using local differential privacy (LDP). Existing LDP protocols are developed …
guarantee using local differential privacy (LDP). Existing LDP protocols are developed …
Gaussian differential privacy
In the past decade, differential privacy has seen remarkable success as a rigorous and
practical formalization of data privacy. This privacy definition and its divergence based …
practical formalization of data privacy. This privacy definition and its divergence based …
Amplification by shuffling: From local to central differential privacy via anonymity
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 usage …
reports done over time. For example, trends in users' private preferences or software usage …
[PDF][PDF] The 2020 census disclosure avoidance system topdown algorithm
ABSTRACT The Census TopDown Algorithm (TDA) is a disclosure avoidance system using
differential privacy for privacy-loss accounting. The algorithm ingests the final, edited version …
differential privacy for privacy-loss accounting. The algorithm ingests the final, edited version …
Deep learning with label differential privacy
Abstract The Randomized Response (RR) algorithm is a classical technique to improve
robustness in survey aggregation, and has been widely adopted in applications with …
robustness in survey aggregation, and has been widely adopted in applications with …
Collecting and analyzing multidimensional data with local differential privacy
Local differential privacy (LDP) is a recently proposed privacy standard for collecting and
analyzing data, which has been used, eg, in the Chrome browser, iOS and macOS. In LDP …
analyzing data, which has been used, eg, in the Chrome browser, iOS and macOS. In LDP …