Privacy-Preserving Global Structural Balance Computation in Signed Networks

The studies on signed networks have received a great attention due to their capabilities on presenting conflicting relationships, which reflect the potential conflicts and tensions of complex systems. To further understand those conflicts and tensions, many methods have been proposed for computing the global structural balance (GSB) of signed networks, which aim to discover the most balanced state of the networks with the least number of unbalanced links. However, most of them request full access to all information (structures, signs, and balance states) of links, which are usually sensitive and private. In this article, we propose a privacy-preserving GSB computation (PGSBC) framework, which aims to compute the GSB while preserving the privacy of the networks. The PGSBC first protects the sensitive information (structures, signs, and balance states) of links by using encryption techniques (the homomorphic cryptosystem and the random disturbances) and then computes the GSB of the signed networks on the encrypted structures. In the PGSBC, a balance-aware energy function is adopted to evaluate the balance degree of a clustering, while a fast two-level greedy algorithm (called as HM-Louvain) is presented to discover the most balanced clustering of signed networks. Simulation results on 11 LFR benchmark networks and 10 real signed networks show that the proposed framework can effectively compute the GSB of the networks while preserving the privacy of links' sensitive information.