In this paper, we consider the problem of anonymization on large networks. There are some anonymization methods for networks, but most of them can not be applied on large networks because of their complexity. We present an algorithm for κ-degree anonymity on large networks. Given a network G, we construct a κ-degree anonymous network, G̃, by the minimum number of edge modifications. We devise a simple and efficient algorithm for solving this problem on large networks. Our algorithm uses univariate micro-aggregation to anonymize the degree sequence, and then it modifies the graph structure to meet the κ-degree anonymous sequence. We apply our algorithm to a different large real datasets and demonstrate their efficiency and practical utility.
|Original language||American English|
|Number of pages||5|
|Journal||Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013|
|Publication status||Published - 2013|