Abstract
Network analysis has benefited greatly from published data of social networks. However, the privacy of users may be compromised even if the data are released after applying anonymization techniques. To measure the resistance against privacy attacks in an anonymous network, Trujillo-Rasua R. et al. introduce the concepts of k-antiresolving set and k-metric antidimension [1]. In this paper, we prove that the problem of k-metric antidimension is NP-hard. We also study the size of k-antiresolving sets in random graphs. Specifically, we establish three bounds on the size of k-antiresolving sets in Erdős-Renyi random graphs.
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