Differential privacy has been a de facto privacy standard in defining privacy and handling
privacy preservation. It has had great success in scenarios of local data privacy and …

The privacy of individuals does not only depend on their own actions and data but may also
be affected by the privacy decisions and by the data shared by other individuals. This …

We propose differential fairness, a multi-attribute definition of fairness in machine learning
which is informed by intersectionality, a critical lens arising from the humanities literature …

We present a rigorous methodology for auditing differentially private machine learning by
adding multiple carefully designed examples called canaries. We take a first principles …

LDP (Local Differential Privacy) has been widely studied to estimate statistics of personal
data (eg, distribution underlying the data) while protecting users' privacy. Although LDP …

The purpose of this paper is to guide interpretation of the semantic privacy guarantees for
some of the major variations of differential privacy, which include pure, approximate, R\'enyi …

Individuals are continually observed by an ever-increasing number of sensors that make up
the Internet of Things. The resulting streams of data, which are analyzed in real time, can …

Differential Privacy (DP) has received increasing attention as a rigorous privacy framework.
Many existing studies employ traditional DP mechanisms (eg, the Laplace mechanism) as …

The use of formal privacy to protect the confidentiality of responses in the 2020 Decennial
Census of Population and Housing has triggered renewed interest and debate over how to …

The prevalence of mobile Internet, smart terminal devices, and GPS positioning technology
has generated a vast number of trajectory data that location-based applications can utilize …