Outsourcing shortest distance computing with privacy protection

With the advent of cloud computing, it becomes desirable to outsource graphs into cloud servers to efficiently perform complex operations without compromising their sensitive information. In this paper, we take the shortest distance computation as a case to investigate the technique issues in outsourcing graph operations. We first propose a parameter-free, edge-based 2-HOP delegation security model (shorten as 2-HOP delegation model), which can greatly reduce the chances of the structural pattern attack and the graph reconstruction attack. We then transform the original graph into a link graph $$G_l$$ kept locally and a set of outsourced graphs $$\mathcal G _o$$. Our objectives include (i) ensuring each outsourced graph meeting the requirement of 2-HOP delegation model, (ii) making shortest distance queries be answered using $$G_l$$ and $$\mathcal G _o$$, (iii) minimizing the space cost of $$G_l$$. We devise a greedy method to produce $$G_l$$ and $$\mathcal G _o$$, which can exactly answer shortest distance queries. We also develop an efficient transformation method to support approximate shortest distance answering under a given average additive error bound. The experimental results illustrate the effectiveness and efficiency of our method.