Distributed Private Online Learning for Social Big Data Computing over Data Center Networks

TL;DR

This paper introduces a distributed sparse online learning algorithm for social big data in data center networks, balancing data mining effectiveness with privacy preservation.

Contribution

It proposes a novel distributed sparse online algorithm that handles large-scale, high-dimensional social data while ensuring privacy protection.

Findings

Sparsity improves algorithm performance.

Privacy-preserving method maintains data utility.

Algorithm effectively processes distributed social data.

Abstract

With the rapid growth of Internet technologies, cloud computing and social networks have become ubiquitous. An increasing number of people participate in social networks and massive online social data are obtained. In order to exploit knowledge from copious amounts of data obtained and predict social behavior of users, we urge to realize data mining in social networks. Almost all online websites use cloud services to effectively process the large scale of social data, which are gathered from distributed data centers. These data are so large-scale, high-dimension and widely distributed that we propose a distributed sparse online algorithm to handle them. Additionally, privacy-protection is an important point in social networks. We should not compromise the privacy of individuals in networks, while these social data are being learned for data mining. Thus we also consider the privacy problem in this article. Our simulations shows that the appropriate sparsity of data would enhance the performance of our algorithm and the privacy-preserving method does not significantly hurt the performance of the proposed algorithm.