Graphs are a widely used data structure for collecting and analyzing relational data. However, when the graph structure is distributed across several parties, its analysis is particularly challenging. In particular, due to the sensitivity of the data each party might want to keep their partial knowledge of the graph private, while still willing to collaborate with the other parties for tasks of mutual benefit, such as data curation or the removal of poisoned data. To address this challenge, we propose Crypto'Graph, an efficient protocol for privacy-preserving link prediction on distributed graphs. More precisely, it allows parties partially sharing a graph with distributed links to infer the likelihood of formation of new links in the future. Through the use of cryptographic primitives, Crypto'Graph is able to compute the likelihood of these new links on the joint network without revealing the structure of the private individual graph of each party, even though they know the number of nodes they have, since they share the same graph but not the same links. Crypto'Graph improves on previous works by enabling the computation of a certain number of similarity metrics without any additional cost. The use of Crypto'Graph is illustrated for defense against graph poisoning attacks, in which it is possible to identify potential adversarial links without compromising the privacy of the graphs of individual parties. The effectiveness of Crypto'Graph in mitigating graph poisoning attacks and achieving high prediction accuracy on a graph neural network node classification task is demonstrated through extensive experimentation on a real-world dataset.

翻译：图是一种广泛用于收集和分析关系数据的数据结构。然而，当图结构分布在多个参与方之间时，其分析尤为具有挑战性。特别是由于各参与方数据的敏感性，他们可能希望在保持对图的部分知识私密的同时，仍愿意与其他方合作完成互利任务，例如数据整理或移除中毒数据。为应对这一挑战，我们提出了Crypto'Graph——一个用于分布式图上隐私保护链路预测的高效协议。具体而言，该协议允许部分共享具有分布式链接的图的参与方推断未来新链接形成的可能性。通过使用密码学原语，Crypto'Graph能够在联合网络上计算这些新链接的可能性，而无需泄露每个参与方私有个体图的结构，尽管他们知道自己拥有的节点数量（因为他们共享同一个图，但链接不同）。与先前工作相比，Crypto'Graph以无额外成本的方式实现了对一定数量相似度度量的计算。我们以防御图投毒攻击为例展示了Crypto'Graph的应用，其中可以在不损害各参与方图隐私的前提下识别潜在的对抗性链接。通过在真实世界数据集上的广泛实验，我们证明了Crypto'Graph在缓解图投毒攻击以及在图神经网络节点分类任务中实现高预测准确率方面的有效性。