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 …

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 …

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 …

The Internet of Things (IoT) is transforming major industries, including but not limited to
healthcare, agriculture, finance, energy, and transportation. IoT platforms are continually …

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 …