The advent of social networks poses severe threats on user privacy as adversaries can de-anonymize users’ identities by mapping them to correlated cross-domain networks. Without ground-truth mapping, prior literature proposes various cost functions in hope of measuring the quality of mappings. However, their cost functions, whose minimizers may remain algorithmically unknown, usually bring imponderable mapping errors when the true mapping cannot minimize these cost functions. We jointly tackle above concerns under a more practical social network model parameterized by overlapping communities , which, neglected by prior art, can serve as side information for de-anonymization. Regarding the unavailability of ground-truth mapping to adversaries, by virtue of the Minimum Mean Square Error (MMSE), our first contribution is a well-justified cost function minimizing the expected number of mismatched users over all possible true mappings. While proving the NP-hardness of minimizing MMSE, we validly transform it into the weighted-edge matching problem (WEMP), which, as disclosed theoretically, resolves the tension between optimality and complexity: 1) WEMP asymptotically returns a negligible mapping error in large network size under mild conditions facilitated by higher overlapping strength; 2) WEMP can be algorithmically characterized via the convex-concave based de-anonymization algorithm (CBDA), effectively finding the optimum of WEMP. Extensive experiments further confirm the effectiveness of CBDA under overlapping communities: 90% users are re-identified averagely in a series of networks when communities overlap densely, and the re-identification ratio is enhanced about 70% compared to non-overlapping cases.

中文翻译：

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具有重叠社区结构的社交网络去匿名化

社交网络的出现对用户隐私构成了严重威胁，因为攻击者可以通过将用户的身份映射到相关的跨域网络来取消匿名。在没有地面真相映射的情况下，现有文献提出了各种成本函数，希望能够衡量映射的质量。但是，当真正的映射无法最小化这些成本函数时，其成本函数（其最小化器可能在算法上仍然未知）通常会带来难以置信的映射错误。我们通过以下参数更实用的社交网络模型共同解决以上问题：重叠的社区 被现有技术所忽略的，可以用作去匿名化的辅助信息。关于对对手的地面真相映射的不可用，凭借最小均方误差（MMSE），我们的第一个贡献是合理化的成本函数，可将所有可能的真实映射上不匹配用户的预期数量降至最低。在证明最小化MMSE的NP难度的同时，我们将其有效地转换为加权边缘匹配问题（WEMP），如理论上所述，它解决了最优性和复杂性之间的矛盾：1）WEMP渐近地返回可忽略的大映射误差较高的重叠强度有助于在温和条件下的网络尺寸；2）可以通过基于凸凹的去匿名算法（CBDA）对WEMP进行算法表征，从而有效地找到最佳WEMP。