Federated learning has been applied to various tasks in intelligent transportation systems to
protect data privacy through decentralized training schemes. The majority of the state-of-the …

Protecting the privacy of individuals in graph structured data while making accurate versions
of the data available is one of the most challenging problems in data privacy. Most efforts to …

A widely used method for estimating counterfactuals and causal treatment effects from
observational data is nearest-neighbor matching. This typically involves pairing each treated …

Organizations and individuals nowadays face increasing daily operations closely rely on a
huge amount of private data which is outsourced to a centralized server. Secure and efficient …

In this article, we present a privacy-preserving technique for user-centric multi-release
graphs. Our technique consists of sequentially releasing anonymized versions of these …

With the development and popularity of social networks, the security issues of personal data
await effective solution. We propose a differential privacy protection scheme for social …

Small datasets form a significant portion of releasable data in high sensitivity domains such
as healthcare. But, providing differential privacy for small dataset release is a hard task …

With the fast development of social networks, there exists a huge amount of users
information as well as their social ties. Such information generally contains sensitive and …

Organizations and individuals nowadays face increasing daily operations closely rely on a
huge amount of private data which is outsourced to a centralized server. Secure and efficient …

With the push of cloud computing which has both resource and compute scalability, data,
which has been exploding in the past years, are often outsourced to a server. To this end …