The notion of Local Differential Privacy (LDP) enables users to answer sensitive questions
while preserving their privacy. The basic LDP frequency oracle protocol enables the …

Data collection under local differential privacy (LDP) has been mostly studied for
homogeneous data. Real-world applications often involve a mixture of different data types …

Federated analytics (FA) is a privacy-preserving framework for computing data analytics
over multiple remote parties (eg, mobile devices) or silo-ed institutional entities (eg …

Local Differential Privacy (LDP) protocols enable an untrusted data collector to perform
privacy-preserving data analytics. In particular, each user locally perturbs its data to …

Naïve Bayes classifier (NBC) is a fundamental and widely-used data mining tool. To
respond to the growing privacy concern, several privacy-preserving NBC schemes have …

By integrating the traditional power grid with information and communication technology,
smart grid achieves dependable, efficient, and flexible grid data processing. The smart …

A large amount of valuable information resides in a decentralized attributed social graph,
where each user locally maintains a limited view of the graph. However, there exists a …

Vector mean estimation is a central primitive in federated analytics. In vector mean
estimation, each user i∈n holds a real-valued vector v_i∈-1,1^d, and a server wants to …

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 …

Next-generation wireless networks (NGWN) aim to support diversified smart applications
that require frequent data exchanges and collaborative data processing among multiple …