The widespread acceptance of differential privacy has led to the publication of many
sophisticated algorithms for protecting privacy. However, due to the subtle nature of this …

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 …

Differential privacy provides the first theoretical foundation with provable privacy guarantee
against adversaries with arbitrary prior knowledge. The main idea to achieve differential …

Releasing full data records is one of the most challenging problems in data privacy. On the
one hand, many of the popular techniques such as data de-identification are problematic …

Differential privacy is a rigorous cryptographically-motivated characterization of data privacy
which may be applied when releasing summaries of a database. Previous work has focused …

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 …

Differential privacy (DP) is a widely accepted mathematical framework for protecting data
privacy. Simply stated, it guarantees that the distribution of query results changes only …

Local Differential Privacy (LDP) provides provable privacy protection for data collection
without the assumption of the trusted data server. In the real-world scenario, different data …

A mutual correlation between trajectories of two users is very helpful to real-life applications
such as product recommendation and social media. While providing tremendous benefits …

When differential privacy was created more than a decade ago, the motivating example was
statistics published by an official statistics agency. In attempting to transition differential …