Paper Link: https://arxiv.org/abs/2312.15591
Github Link: https://github.com/HKUST-KnowComp/PrivateNGDB.
In the fast-evolving world of data management, the intersection of graph databases and neural networks has led to the development of Neural Graph Databases (NGDBs). These systems excel in handling complex, graph-structured data, making them invaluable for applications like recommendation systems and fraud detection. However, with great power comes great responsibility, especially when it comes to safeguarding privacy.
The Rise of NGDBs
NGDBs are a groundbreaking combination of graph databases and neural networks. They leverage neural embedding storage and complex query answering to provide efficient storage, retrieval, and analysis of interconnected data. This capability allows NGDBs to fill in gaps in data, revealing hidden relationships through advanced generalization techniques.
Privacy Concerns
While NGDBs offer powerful generalization abilities, they also pose significant privacy risks. Malicious actors can exploit these capabilities to infer sensitive information, even if direct queries for such data are restricted. For example, by crafting specific queries, attackers might uncover private information about individuals, such as their living locations, which should remain confidential.
Introducing Privacy-Preserved NGDBs
To tackle these privacy challenges, the paper proposes a Privacy-Preserved Neural Graph Database (P-NGDB) framework. This system incorporates adversarial training techniques to generate indistinguishable answers when queried with potentially private information. The goal is to enhance privacy protection while maintaining the quality of public data retrieval.
Key Contributions
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Pioneering Privacy Protection: This research is among the first to address privacy leakage in NGDBs, providing formal definitions and protection strategies.
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Benchmark Development: A new benchmark on three datasets evaluates the balance between query answering performance and privacy protection.
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Adversarial Techniques: By introducing adversarial training, P-NGDBs can effectively obscure sensitive information without compromising data utility.
Conclusion
In an era where data privacy is paramount, the introduction of P-NGDBs marks a significant advancement in protecting sensitive information within neural graph databases. This framework not only shields private data but also enhances the robustness of data retrieval, setting a new standard for secure and efficient data management in the age of large language models.