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Privacy-Preserving Knowledge Graph Databases: A Hybrid Framework for Secure AI-Driven Semantic Data Management

Nithin Reddy Gadicharla

Abstract


Study Problem and Context

Combining knowledge graphs with artificial intelligence has transformed semantic data management systems to permit powerful analytics and reasoning among interrelated data. Nevertheless, the synergy poses great privacy risks since AI models usually need direct access to plaintext sensitive data to train and make inferences. This inherent dilemma on the usefulness of semantic data and privacy is a razor case issue in an area where confidential knowledge is involved, such as healthcare, financial services, and government apparatus.

Planned Solution (PP-KGDB Framework)


This article proposes a brand new Privacy-Preserving Knowledge Graph Database model, PP-KGDB, which allows comfortable semantic data management with AI using the privacy protection offered by the novel knowledge graph framework. With our solution, we offer a single architecture that preserves the full expressive capability of knowledge graphs and one in that end-to-end privacy is assured, with cryptographic protection of the entire computation process.

Technical Method (Hybrid FHE-SMPC Architecture)

The sophisticated hybrid architecture used by the PP-KGDB involves Fully Homomorphic encryption (FHE) and Secure Multi-Party Computation (SMPC). The system has three built-in layers: (1) a semantic layer manages the knowledge graphs structure via property graphs and ontologies, (2) an encryption layer, an implementation of hybrid cryptographic protocols with sophisticated key management, and (3) a secure computation layer, which performs AI operations on encrypted data in optimized FHE-SMPC workflows. With this, you can perform complex semantic queries and machine learning inferences and still ensure the data is encrypted during processing.


Major Results and Performance Measures

The framework is effective as evidenced by experimentation on two practical applications - secure medical diagnosis and financial anomaly detection. The system ensures model accuracy of 1.8% of plaintext baselines with formal cryptographic security assurances. Performance analysis indicates that on complex semantic AI queries measures 2.3-4.7 seconds query latency, or 15-25x overhead over plaintext processing, a fair tradeoff to demand strong privacy. Key prerequisites the framework performs graph knowledge processing with up to 50,000 entities and an overall linear dependency on graph size.

 

Primary Investigations and Conclusions

The main contributions of this study are: (1) the unified architecture of privacy-preserving knowledge graph databases, (2) the existence of a hybrid FHE-SMPC for the learning to protect privacy work on semantic data, (3) the first fully functional implementation and help worth of such a database, and (4) an in-depth analysis of the privacy-utility-performance tradeoffs. The PP-KGDB system allows companies to apply cutting-edge AI technology to sensitive semantic information and comply with strict privacy laws, new avenues of safe cross-institutional collaborative analytics are made possible.


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References


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