Differential privacy has been a de facto privacy standard in defining privacy and handling
privacy preservation. It has had great success in scenarios of local data privacy and …

The protocols that satisfy Local Differential Privacy (LDP) enable untrusted third parties to
collect aggregate information about a population without disclosing each user's privacy. In …

Local Differential Privacy (LDP) is now widely adopted in large-scale systems to collect and
analyze sensitive data while preserving users' privacy. However, almost all LDP protocols …

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), which enables an untrusted server to collect aggregated
statistics from distributed users while protecting the privacy of those users, has been widely …

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) has been widely used to collect sensitive data from
distributed users while preserving individual privacy. However, very recent studies show that …

The rise in IoT-driven distributed data analytics, coupled with increasing privacy concerns,
has led to a demand for effective privacy-preserving and federated data collection/model …

Local differential privacy (LDP) provides a way for an untrusted data collector to aggregate
users' data without violating their privacy. Various privacy-preserving data analysis tasks …

With the exponential growth of data and its crucial impact on our lives and decision-making,
the integrity of data has become a significant concern. Malicious data poisoning attacks …