论文标题
隐私保护链接预测
Privacy-Preserving Link Prediction
论文作者
论文摘要
考虑两个数据持有人ABC和XYZ，其中包含图形数据（例如社交网络，电子商务，电信和生物信息学）。 ABC可以看到节点A链接到节点B，XYZ可以看到节点B链接到节点C。节点B是A和C的常见邻居，但是两个网络都无法自行发现这一事实。在本文中，我们提供了一个两方计算，ABC和XYZ可以运行，以发现其图形数据结合中的共同邻居，但是任何一方都必须向另一方揭示其明文图。基于私人集交叉点，我们实施解决方案，提供测量并量化部分隐私泄漏。我们还提出了一种重量级解决方案，该解决方案可根据添加性同态加密泄漏零信息。
Consider two data holders, ABC and XYZ, with graph data (e.g., social networks, e-commerce, telecommunication, and bio-informatics). ABC can see that node A is linked to node B, and XYZ can see node B is linked to node C. Node B is the common neighbour of A and C but neither network can discover this fact on their own. In this paper, we provide a two party computation that ABC and XYZ can run to discover the common neighbours in the union of their graph data, however neither party has to reveal their plaintext graph to the other. Based on private set intersection, we implement our solution, provide measurements, and quantify partial leaks of privacy. We also propose a heavyweight solution that leaks zero information based on additively homomorphic encryption.
