Introduction: Privacy-preserving computation is a crucial discipline that protects the privacy of confidential data while facilitating secure analysis. In graph problems, secure multiparty computation (SMC) presents a promising approach to performing these functionalities. This article delves into the notable advancements in privacy-preserving parallel algorithms recently introduced in a Ph.D. thesis. By integrating graph algorithms with […]

We are living in a world in which data is not just getting bigger; it is also getting more and more interconnected. Large graph analytics is a useful tool for gaining insights from large volumes of interconnected data and has applications in various fields such as social media, biology, and telecommunications. An example of the […]