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 …

The effective physical data sharing has been facilitating the functionality of Industrial IoTs,
which is believed to be one primary basis for Industry 4.0. These physical data, while …

The Internet of Vehicles (IoV) is a promising branch of the Internet of Things. IoV simulates a
large variety of crowdsourcing applications, such as Waze, Uber, and Amazon Mechanical …

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 …

We consider the problem of designing scalable, robust protocols for computing statistics
about sensitive data. Specifically, we look at how best to design differentially private …

Protocols satisfying Local Differential Privacy (LDP) enable parties to collect aggregate
information about a population while protecting each user's privacy, without relying on a …

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 …