The sharing of genomic data holds great promise in advancing precision medicine and
providing personalized treatments and other types of interventions. However, these …

The goal of privacy metrics is to measure the degree of privacy enjoyed by users in a system
and the amount of protection offered by privacy-enhancing technologies. In this way, privacy …

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

Differential privacy comes equipped with multiple analytical tools for the design of private
data analyses. One important tool is the so-called" privacy amplification by subsampling" …

Deep learning techniques based on neural networks have shown significant success in a
wide range of AI tasks. Large-scale training datasets are one of the critical factors for their …

Deep learning models are often trained on datasets that contain sensitive information such
as individuals' shopping transactions, personal contacts, and medical records. An …

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 …

Many modern cities strive to integrate information technology into every aspect of city life to
create so-called smart cities. Smart cities rely on a large number of application areas and …

The wide-spread availability of rich data has fueled the growth of machine learning
applications in numerous domains. However, growth in domains with highly-sensitive data …

The data collected in Internet of Thing (IoT) systems (IoT data) have stimulated dramatic
extension to the boundary of commercialized data statistic analysis, owing to the pervasive …