Users' privacy is vulnerable at all stages of the deep learning process. Sensitive information
of users may be disclosed during data collection, during training, or even after releasing the …

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 …

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 …

We demonstrate that it is possible to train large recurrent language models with user-level
differential privacy guarantees with only a negligible cost in predictive accuracy. Our work …

The collection and analysis of telemetry data from user's devices is routinely performed by
many software companies. Telemetry collection leads to improved user experience but …

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 …

The large-scale monitoring of computer users' software activities has become commonplace,
eg, for application telemetry, error reporting, or demographic profiling. This paper describes …

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 …

Recent work of Erlingsson, Feldman, Mironov, Raghunathan, Talwar, and Thakurta 1
demonstrates that random shuffling amplifies differential privacy guarantees of locally …

Local differential privacy (LDP), where users randomly perturb their inputs to provide
plausible deniability of their data without the need for a trusted party, has been adopted …