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 …

Split learning (SL) aims to protect user data privacy by distributing deep models between the
client-server and kee** private data locally. Only processed or 'smashed'data can be …

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

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 …

With the growing popularity of local differential privacy (LDP), there is increasing interest in
its deployment in industrial applications, smart homes, and smart cities. However, the main …

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 …