Privacy has become one of the fundamental concerns in the industrial deployment of
machine learning algorithms. While designing adversarial attacks reveals the potential level …

In the past decade, data privacy, security, and trustworthiness have gained tremendous
attention from research communities, and these are still active areas of research with the …

There is a huge database which contains some private and public information. When we are
mining the useful information from the enterprise data, there can be issues regarding …

Data mining is a recent advance technology with great potential to help companies focus on
the crucial information in the data they have collected about the behavior of their customers …

We consider the privacy-preserving computation of node influence in distributed social
networks, as measured by egocentric betweenness centrality (EBC). Motivated by modern …

Organizations today collect and aggregate huge amounts of data from individuals under
various scenarios and for different purposes. Such aggregation of individuals' data when …

Abstract In the Journal of Cryptology (25 (1): 158-193. 2012), Shai Halevi and Yael Kalai
proposed a general framework for constructing two-message oblivious transfer protocols …

Privacy-preserving distributed data mining is the study of mining on distributed data—owned
by multiple data owners—in a non-secure environment, where the mining protocol does not …

In many areas of science and industry, the volume of data that is collected and stored has
increased significantly over the past years (Marx, 2013). Statistical and machine learning …

In the era where big data is the new norm, a higher emphasis has been placed on models
which guarantees the release and exchange of data. The need for privacy-preserving data …