In the era of big data and rapidly evolving information systems, efficient and accurate data retrieval has become increasingly crucial. Neural graph databases (NGDBs) have emerged as a powerful paradigm that combines the strengths of graph databases (graph DBs) and neural networks to enable efficient storage, retrieval, and analysis of graph-structured data. The usage of neural embedding storage and complex neural logical query answering provides NGDBs with generalization ability. When the graph is incomplete, by extracting latent patterns and representations, neural graph databases can fill gaps in the graph structure, revealing hidden relationships and enabling accurate query answering. Nevertheless, this capability comes with inherent trade-offs, as it introduces additional privacy risks to the database. Malicious attackers can infer more sensitive information in the database using well-designed combinatorial queries, such as by comparing the answer sets of where Turing Award winners born before 1950 and after 1940 lived, the living places of Turing Award winner Hinton are probably exposed, although the living places may have been deleted in the training due to the privacy concerns. In this work, inspired by the privacy protection in graph embeddings, we propose a privacy-preserving neural graph database (P-NGDB) to alleviate the risks of privacy leakage in NGDBs. We introduce adversarial training techniques in the training stage to force the NGDBs to generate indistinguishable answers when queried with private information, enhancing the difficulty of inferring sensitive information through combinations of multiple innocuous queries. Extensive experiment results on three datasets show that P-NGDB can effectively protect private information in the graph database while delivering high-quality public answers responses to queries.

翻译：在大数据与快速演进的信息系统时代，高效准确的数据检索变得日益关键。神经图数据库（NGDB）作为一种新兴范式，融合了图数据库（图DB）与神经网络的各自优势，能够实现图结构数据的高效存储、检索与分析。通过采用神经嵌入存储与复杂神经逻辑查询回答机制，NGDB具备泛化能力。当图数据不完整时，神经图数据库可借助提取潜在模式与表示，填补图结构中的空缺，揭示隐藏关系，实现精确的查询回答。然而，这一能力也带来了固有的权衡，因为它增加了数据库的隐私风险。恶意攻击者可通过精心设计的组合查询（例如，通过比较1940年后与1950年前出生的图灵奖得主的居住地答案集），推断出数据库中的敏感信息——即使因隐私顾虑已在训练阶段删除了居住地数据，图灵奖得主辛顿的居住地仍可能被暴露。本研究受图嵌入隐私保护启发，提出一种隐私保护神经图数据库（P-NGDB），以缓解NGDB中的隐私泄露风险。我们在训练阶段引入对抗训练技术，迫使NGDB在被查询隐私信息时生成难以区分的答案，从而增强通过多个无害查询组合推断敏感信息的难度。在三个数据集上的大量实验结果表明，P-NGDB在向查询提供高质量公开回答的同时，能够有效保护图数据库中的隐私信息。