Knowledge Graph Embedding (KGE) is a fundamental technique that extracts expressive representation from knowledge graph (KG) to facilitate diverse downstream tasks. The emerging federated KGE (FKGE) collaboratively trains from distributed KGs held among clients while avoiding exchanging clients' sensitive raw KGs, which can still suffer from privacy threats as evidenced in other federated model trainings (e.g., neural networks). However, quantifying and defending against such privacy threats remain unexplored for FKGE which possesses unique properties not shared by previously studied models. In this paper, we conduct the first holistic study of the privacy threat on FKGE from both attack and defense perspectives. For the attack, we quantify the privacy threat by proposing three new inference attacks, which reveal substantial privacy risk by successfully inferring the existence of the KG triple from victim clients. For the defense, we propose DP-Flames, a novel differentially private FKGE with private selection, which offers a better privacy-utility tradeoff by exploiting the entity-binding sparse gradient property of FKGE and comes with a tight privacy accountant by incorporating the state-of-the-art private selection technique. We further propose an adaptive privacy budget allocation policy to dynamically adjust defense magnitude across the training procedure. Comprehensive evaluations demonstrate that the proposed defense can successfully mitigate the privacy threat by effectively reducing the success rate of inference attacks from $83.1\%$ to $59.4\%$ on average with only a modest utility decrease.

翻译：知识图谱嵌入（KGE）是一项基础技术，它从知识图谱中提取具有表达能力的表征，以支持多样的下游任务。新兴的联邦知识图谱嵌入（FKGE）通过协作训练分布在客户端之间的知识图谱，同时避免交换客户端敏感的原始知识图谱数据，但这仍可能面临其他联邦模型训练（例如神经网络）中已证实的隐私威胁。然而，对于具有独特属性（不同于先前研究的模型）的FKGE而言，量化并防御此类隐私威胁仍属未探索领域。本文首次从攻击与防御双重视角对FKGE中的隐私威胁进行系统性研究。在攻击方面，我们通过提出三种新的推理攻击来量化隐私威胁，这些攻击通过成功推断受害者客户端中知识图谱三元组的存在性，揭示了显著的隐私风险。在防御方面，我们提出DP-Flames——一种具有私有选择机制的新型差分隐私FKGE方法，该方法利用FKGE的实体绑定稀疏梯度特性实现了更优的隐私-效用权衡，并结合了最先进的私有选择技术，提供了紧密的隐私核算。此外，我们还提出一种自适应隐私预算分配策略，以在训练过程中动态调整防御强度。综合评估表明，所提出的防御方法能够成功缓解隐私威胁，在仅轻微降低效用的条件下，将推理攻击的平均成功率从83.1%有效降低至59.4%。