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 …

Local differential privacy (LDP) is promising for private streaming data collection and
analysis. However, existing few LDP studies over streams either apply to finite streams only …

This paper investigates the problem of collecting multidimensional data throughout time (ie,
longitudinal studies) for the fundamental task of frequency estimation under Local …

The collection of user attributes by service providers is a double-edged sword. They are
instrumental in driving statistical analysis to train more accurate predictive models like …

Automated decision systems are increasingly used to make consequential decisions in
people's lives. Due to the sensitivity of the manipulated data and the resulting decisions …

This paper introduces the multi-freq-ldpy Python package for multiple frequency estimation
under Local Differential Privacy (LDP) guarantees. LDP is a gold standard for achieving …

Graph Neural Networks have achieved tremendous success in modeling complex graph
data in a variety of applications. However, there are limited studies investigating privacy …

We describe a new setting for learning a deep graph generative model (GGM) from
aggregate graph statistics, rather than from the graph adjacency matrix. Matching the …

Collecting and analyzing evolving longitudinal data has become a common practice. One
possible approach to protect the users' privacy in this context is to use local differential …

The private collection of multiple statistics from a population is a fundamental statistical
problem. One possible approach to realize this is to rely on the local model of differential …