Privacy Preservation in Social Networks with Sensitive Edge Weights
Privacy Preservation in Social Networks with Sensitive Edge Weights
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DOI：
10.1137/1.9781611972795.82
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
Lian Liu;Jie Wang;Jinze Liu;Jun Zhang
中科院分区：
文献类型：
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作者：
Lian Liu;Jie Wang;Jinze Liu;Jun Zhang
随着Facebook、MySpace等新兴社交网络的发展，社交网络分析带来的安全和隐私威胁，在社交网络数据共享或公开的过程中，带来了泄露机密知识的风险。除了当前的社交网络匿名去识别技术之外，我们还研究了一种情况，例如在商业交易网络中，权重被附加到被认为是机密的网络边缘（例如，交易）。我们考虑在网络发布时扰动某些边的权值以保护数据隐私，同时保留原始网络中某些节点对之间的最短路径和路径的近似代价。我们为此应用程序开发了两种隐私保护策略。第一种策略是基于高斯随机化乘法，第二种策略是基于图论的贪婪摄动算法。特别是，第二种策略不仅在保持所选节点对之间的最短路径的同时产生最短路径的近似长度，而且最大限度地保持了原始权重的隐私性。我们给出了实验结果来支持我们的数学分析。
With the development of emerging social networks, such as Facebook and MySpace, security and privacy threats arising from social network analysis bring a risk of disclosure of confidential knowledge when the social network data is shared or made public. In addition to the current social network anonymity de-identification techniques, we study a situation, such as in a business transaction network, in which weights are attached to network edges that are considered to be confidential (e.g., transactions). We consider perturbing the weights of some edges to preserve data privacy when the network is published, while retaining the shortest path and the approximate cost of the path between some pairs of nodes in the original network. We develop two privacypreserving strategies for this application. The first strategy is based on a Gaussian randomization multiplication, the second one is a greedy perturbation algorithm based on graph theory. In particular, the second strategy not only yields an approximate length of the shortest path while maintaining the shortest path between selected pairs of nodes, but also maximizes privacy preservation of the original weights. We present experimental results to support our mathematical analysis.