Personalized PageRank (PPR) is a fundamental tool in unsupervised learning of graph representations such as node ranking, labeling, and graph embedding. However, while data privacy is one of the most important recent concerns, existing PPR algorithms are not designed to protect user privacy. PPR is highly sensitive to the input graph edges: the difference of only one edge may cause a big change in the PPR vector, potentially leaking private user data. In this work, we propose an algorithm which outputs an approximate PPR and has provably bounded sensitivity to input edges. In addition, we prove that our algorithm achieves similar accuracy to non-private algorithms when the input graph has large degrees. Our sensitivity-bounded PPR directly implies private algorithms for several tools of graph learning, such as, differentially private (DP) PPR ranking, DP node classification, and DP node embedding. To complement our theoretical analysis, we also empirically verify the practical performances of our algorithms.

翻译：个性化PageRank（PPR）是无监督图表示学习（如节点排序、标记和图嵌入）中的基础工具。然而，尽管数据隐私是近年来最重要的关注点之一，现有PPR算法并未设计用于保护用户隐私。PPR对输入图边高度敏感：仅一条边的差异就可能导致PPR向量发生较大变化，从而可能泄露用户隐私数据。本文提出了一种算法，该算法输出近似PPR，并对输入边的敏感性具有可证明的有界性。此外，我们证明了当输入图度数较大时，该算法能达到与非隐私算法相当的精度。基于敏感性约束的PPR可直接应用于多种图学习工具的隐私保护算法，例如差分隐私（DP）PPR排序、DP节点分类和DP节点嵌入。为补充理论分析，我们还通过实验验证了算法的实际性能。