Abstract
Privacy in social networks has been a vast active area of research due to the enormous increase in privacy concerns with social networking services. Social networks contain sensitive information of individuals, which could be leaked due to insecure data sharing. To enable a secure social network data publication, several privacy schemes were proposed and built upon the anonymity of users. In this paper, we incorporate unlinkability in the context of weighted network data publication, which has not been addressed in prior work. Two key privacy models are defined, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">edge weight unlinkability</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">node unlinkability</i> to obviate the linking of auxiliary information to a targeted individual with high probability. Two new schemes satisfying these unlinkability notions, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MinSwap</i> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-MinSwapX</i> are proposed to address edge weight disclosure, link disclosure and identity disclosure problems in publishing weighted network data. The edge weight is modified based on minimum value change in order to preserve the usefulness and properties of the edge weight data. In addition, edge randomization is performed to minimally modify the structural information of a user. Experimental results on real data sets show that our schemes efficiently achieve data utility preservation and privacy protection simultaneously.
Highlights
I N recent years, social networks such as Facebook, TikTok, WeChat, LinkedIn, Netflix, Google and Instagram have gained tremendous popularity as these networks support a variety of attractive features and services that help to connect the people
4) Overall Discussion on Structural Anonymization The privacy protection is guaranteed in the structural anonymization schemes above
This is achieved by considering edge deletion based on the importance of edges in the original network, such that essential edges are preserved in the published data
Summary
I N recent years, social networks such as Facebook, TikTok, WeChat, LinkedIn, Netflix, Google and Instagram have gained tremendous popularity as these networks support a variety of attractive features and services that help to connect the people. There have been considerable interest in preserving privacy of network users associated to data publication, especially on edge weight disclosure [21]–[32], link disclosure [33]–[49] and identity disclosure problems [27], [28], [31], [32], [36]–[57]. These schemes rely upon edge weight and structural information as the background knowledge to attack the privacy of a user. Unlinkability provides higher privacy protection but has not been considered in the context of weighted network data publication
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