| Summary: | 碩士 === 國立高雄大學 === 資訊管理學系碩士班 === 101 === Preserving privacy in social networking environment has been studied extensively in recent years. Although more works have adopted un-weighted graphs to model network relationships, weighted graph modeling can provide deeper analysis of the degree of relationships. Previous works on weighted graph privacy have concentrated on preserving the shortest path characteristic between pairs of vertices. Two common types of privacy have been proposed. One type of privacy tried to add random noise edge weights to the graph but still maintain the same shortest path. The other privacy, k–anonymous path privacy, minimally perturbed edge weights so that there exist k shortest paths. However, the k-shortest path privacy did not consider degree attacks on the nodes of anonymized shortest paths. In this work, we present a new concept called (k1, k2)-shortest path privacy to prevent such privacy breach. A published network graph with (k1, k2)-shortest path privacy has at least k1 indistinguishable shortest paths between the source and destination vertices. In addition, for the non-overlapping vertices on the k1 shortest paths, there exits k2 vertices with the same node degree and lie on more than one shortest path. Three heuristic algorithms and two edge insertion techniques are proposed. The weight determination technique is also proposed to effectively achieve the proposed privacy. Numerical comparisons based on four evaluative criterions showing the pros and cons of each technique are presented.
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