Multiple Private Networks Research Articles

Local Community Detection in Multiple Private Networks

Individuals are often involved in multiple online social networks. Considering that owners of these networks are unwilling to share their networks, some global algorithms combine information from multiple networks to detect all communities in multiple networks without sharing their edges. When data owners are only interested in the community containing a given node, it is unnecessary and computationally expensive for multiple networks to interact with each other to mine all communities. Moreover, data owners who are specifically looking for a community typically prefer to provide less data than the global algorithms require. Therefore, we propose the Local Collaborative Community Detection problem (LCCD). It exploits information from multiple networks to jointly detect the local community containing a given node without directly sharing edges between networks. To address the LCCD problem, we present a method developed from M method, called colM, to detect the local community in multiple networks. This method adopts secure multiparty computation protocols to protect each network’s private information. Our experiments were conducted on real-world and synthetic datasets. Experimental results show that colM method could effectively identify community structures and outperform comparison algorithms.

Privacy-Preserving Link Prediction in Multiple Private Networks

In many cases, a network may be dispersedly recorded by different participants and each participant is one part of the original network, and no one is willing to share its data due to commercial competition. Therefore, each participant forms an independent private network, and they form a “ multiple private networks” regarding the original network. Existing methods only use the structure of private network itself to predict missing links, leading to underutilized information and deteriorated prediction accuracy. One natural question arises: how to integrate the information of multiple private networks by formulating a security protocol, so as to help each private network to better predict missing links in its own network without disclosing its structure to others. To this end, we propose an SMPC-LP method based on secure multiparty computation (SMPC) to solve this problem. The method fuses the information of each private network without disclosing their inputs, and then, the similarity score of each node pair is jointly calculated, achieving enhanced link prediction performance in each private network. The experimental results show that the SMPC-LP method can better predict the missing links than the methods only using the information of the one private network, without violating data privacy agreement.

Collaborative Detection of Community Structure in Multiple Private Networks

In real-world applications, each data owner might have only partial information of the complete social networks. They wish to find the community structure within multiple networks but without sharing their data directly. However, the existing works on collaborative community detection rarely consider the edges privacy issue in the networks. In this article, from the view of secure multiparty computation, we present two methods to detect the community structure of the multiple networks without directly exchanging edges’ information. These two methods are developed from the fast modularity algorithm ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$fastModular$ </tex-math></inline-formula> ) and the label propagation algorithm (LPA), and they are called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$CofastModular$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$CoLPA$ </tex-math></inline-formula> , respectively. Both methods can detect the community structure within multiple networks without the need to directly exchange the edges’ information. Experiments are conducted on several real-world and synthetic networks. Experimental results show that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$CofastModular$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$CoLPA$ </tex-math></inline-formula> could identify community structure effectively.