As a popular machine learning method, federated learning (FL) can effectively solve the
issues of data silos and data privacy. However, traditional federated learning schemes …

The collection of user attributes by service providers is a double-edged sword. They are
instrumental in driving statistical analysis to train more accurate predictive models like …

Differential privacy has recently become a widely recognized strict privacy protection model
of data release. Differential privacy histogram publishing can directly show the statistical …

Data aggregation has been widely researched to address the privacy concern when data is
published, meanwhile, data aggregation only obtains the sum or average in an area. In …

The industrial Internet connects equipment to the network and utilizes the data generated to
assist businesses. Industrial big data is the result of data accumulation; thus, the industrial …

Distributed federated learning models are vulnerable to membership inference attacks (MIA)
because they remember information about their training data. Through a comprehensive …

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 …

Graph analysts cannot directly obtain the global structure in decentralized social networks,
and analyzing such a network requires collecting local views of the social graph from …

We consider the problem of privately estimating the mean of vectors distributed across
different nodes of an unreliable wireless network, where communications between nodes …

Aiming at the problem of adopting the same level of privacy protection for sensitive data in
the process of data collection and ignoring the difference in privacy protection requirements …