保护知识图谱隐私，防止信息泄露的同时保留可用知识。
Chase Anonymisation: Privacy-Preserving Knowledge Graphs with Logical Reasoning
- 通过增删节点边、重命名和权重扰动实现隐私保护
- 在真实数据集上验证隐私保护效果，同时保持业务语义可用性
- 适合需要共享知识图谱但严守数据隐私的场景
我们提出一种新框架，实现知识图谱（KGs）共享的同时，确保应保密的信息既不直接公开，也不通过推导知识间接暴露，同时保留图谱中的嵌入知识以支持下游业务任务。该方法通过受控地增加节点与边、重命名节点及扰动权重，对输入图谱进行增强，生成隐私保护后的知识图谱。我们引入一种新的知识图谱隐私度量方式，考虑推导出的知识；提出一种新的效用指标，捕捉希望保留的业务语义；并设计两种新型匿名化算法。通过在合成图与真实世界数据集上的广泛实验评估，验证了该方法的有效性。
原文摘要 · Abstract (English)
We propose a novel framework to enable Knowledge Graphs (KGs) sharing while ensuring that information that should remain private is not directly released nor indirectly exposed via derived knowledge, maintaining at the same time the embedded knowledge of the KGs to support business downstream tasks. Our approach produces a privacy-preserving KG as an augmentation of the input one via controlled addition of nodes and edges as well as re-labeling of nodes and perturbation of weights. We introduce a novel privacy measure for KGs, which considers derived knowledge, a new utility metric that captures the business semantics we want to preserve, and propose two novel anonymisation algorithms. Our extensive experimental evaluation, with both synthetic graphs and real-world datasets, confirms the effectiveness of our approach.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。