The advancement of data communication technologies promotes widespread data collection
and transmission in various application domains, thereby expanding big data significantly …

The collection and analysis of telemetry data from user's devices is routinely performed by
many software companies. Telemetry collection leads to improved user experience but …

The application of graph analytics to various domains has yielded tremendous societal and
economical benefits in recent years. However, the increasingly widespread adoption of …

In local differential privacy (LDP), each user perturbs her data locally before sending the
noisy data to a data collector. The latter then analyzes the data to obtain useful statistics …

Concerns on location privacy frequently arise with the rapid development of GPS enabled
devices and location-based applications. While spatial transformation techniques such as …

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 …

Shortly after it was first introduced in 2006, differential privacy became the flagship data
privacy definition. Since then, numerous variants and extensions were proposed to adapt it …

Graph data publishing under node-differential privacy (node-DP) is challenging due to the
huge sensitivity of queries. However, since a node in graph data oftentimes represents a …

Differential privacy is considered a de facto standard for private data analysis. However, the
definition and much of the supporting literature applies to flat tables. While there exist …

Differential privacy provides a rigorous standard for evaluating the privacy of perturbation
algorithms. It has widely been regarded that differential privacy is a universal definition that …