Intellectual Property India Publishes Patent Application for 'Quantum-Resistant Privacy-Preserving Data Vault With Homomorphic Encryption And AI-Driven Data Anonymization For Secure Multi-Party Computation' Filed by Apexcognita Private Limited

MUMBAI, India, April 10 -- Intellectual Property India has published a patent application (202441075316 A) filed by Apexcognita Private Limited, Hyderabad, Telangana, on Oct. 4, 2024, for 'quantum-resistant privacy-preserving data vault with homomorphic encryption and ai-driven data anonymization for secure multi-party computation.'

Inventor(s) include Babu Siva Prasad Dachepalli.

The application for the patent was published on April 10, under issue no. 15/2026.

According to the abstract released by the Intellectual Property India: "The Privacy-Preserving Data Vault (PPDV) (100) is an advanced secure storage and computation system that integrates quantum-resistant encryption, homomorphic computation, and AI-driven data anonymization to enable secure data storage, sharing, and analysis while preserving privacy. The system employs a quantum-resistant encryption layer (102), a homomorphic computation engine (104) for processing encrypted data, and a secure multi-party computation protocol (110) for collaborative analysis. Its AI-driven data anonymization engine (106) dynamically adjusts privacy levels, while a distributed storage architecture ensures data integrity and availability. The PPDV includes a quantum-safe key management system (112) and a regulatory compliance framework, making it a comprehensive solution for organizations handling sensitive data in finance, healthcare, research, and government sectors. The system features an extensible encryption framework, adaptive anonymization techniques, and a scalable storage architecture, complemented by hybrid classical-quantum encryption and geo-aware access controls, ensuring robust privacy protection and regulatory compliance in an evolving data protection landscape. 12. DESCRIPTION OF THE DRAWINGS Fig. 1: Overall System Architecture of PPDV Fig. 2: Quantum-Resistant Encryption Layer Fig. 3: Homomorphic Computation Engine Fig. 4: Secure Multi-Party Computation Protocol Fig. 5: AI-Driven Data Anonymization Engine Fig. 6: Dynamic Access Control Mechanism Fig. 7: Distributed Storage Architecture Fig. 8: Quantum-Safe Key Management System Fig. 9: Regulatory Compliance Framework Fig. 10: Scalability Scenarios Fig. 11: Quantum Resilience Strategy Fig. 12: Privacy-Preserving Computation Techniques Fig. 13: AI Ethics and Bias Mitigation in Data Anonymization Fig. 14: Secure Data Sharing and Collaboration Fig. 15: Continuous Compliance Monitoring and Reporting 13. DRAWINGS (Refer to separate sheets for detailed diagrams)."

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