I study two privacy-preserving social network graphs to dis- close the types of relationships of connecting edges and provide flexible multigrained access control. To create such graphs, my schemes employ the concept of secretaries and types of relationships. It is significantly more efficient than those that using expensive cryptographic primitives. I also show how these schemes can be used for multigrained access control with various options. In addition, I describe how much these schemes are resilient to infer the types of connecting edges.
Zou and Pei @cite_12 described an algorithm for the subgraph created by the immediate @math neighbors of a node based on greedy heuristics. They defined @math -neighborhood anonymity of a graph. Given a graph @math , for each node @math , the algorithm finds @math neighbor nodes for @math . For every pair of node @math and for each pair @math , the algorithm modifies the neighborhood subgraph of @math and the neighborhood subgraph of @math to make them isomorphic to each other. However, this algorithm is heuristic, moreover, expensive approach.
@cite_12
2096296626
Recently, as more and more social network data has been published in one way or another, preserving privacy in publishing social network data becomes an important concern. With some local knowledge about individuals in a social network, an adversary may attack the privacy of some victims easily. Unfortunately, most of the previous studies on privacy preservation can deal with relational data only, and cannot be applied to social network data. In this paper, we take an initiative towards preserving privacy in social network data. We identify an essential type of privacy attacks: neighborhood attacks. If an adversary has some knowledge about the neighbors of a target victim and the relationship among the neighbors, the victim may be re-identified from a social network even if the victim's identity is preserved using the conventional anonymization techniques. We show that the problem is challenging, and present a practical solution to battle neighborhood attacks. The empirical study indicates that anonymized social networks generated by our method can still be used to answer aggregate network queries with high accuracy.