Machine learning models are known to memorize private data to reduce their training loss,
which can be inadvertently exploited by privacy attacks such as model inversion and …

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 …

While collecting data from a large population, local differential privacy (LDP), which only
sends users' perturbed data to the data collector, becomes a popular solution to preserving …

With the prevalence of data-driven intelligence, data markets with various data products are
gaining considerable interest as a promising paradigm for commoditizing data and …

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 …