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 …

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 …

Local Differential Privacy (LDP) allows answering queries on users data while maintaining
their privacy. Queries are often issued on multidimensional datasets with categorical and …

Spatial decompositions are commonly used in the privacy literature for various purposes
such as range query answering, spatial indexing, count-of-counts histograms, data …

Set-valued data are commonly used to represent subsets of a universal set and are
frequently utilized in online services, such as online shopping preferences, website …

Local differential privacy (LDP) is a promising technique to realize privacy-preserving data
aggregation without a trusted aggregator. Normally, an LDP protocol requires each user to …

Local differential privacy (LDP) is a state-of-the-art privacy notion that enables terminal
participants to share their private data safely while controlling the privacy disclosure at the …

This paper introduces the LDP-Auditor framework for empirically estimating the privacy loss
of Locally Differentially Private (LDP) mechanisms. Several factors influencing the privacy …

While the existing literature on Differential Privacy (DP) auditing predominantly focuses on
the centralized model (eg, in auditing the DP-SGD algorithm), we advocate for extending this …