Privacy Network Trust Model for Secure Data Sharing

The patent segments the centralized enterprise-centric security model into a distributed network of trust anchors and policy enforcement points. Each organization maintains its own trust anchors and policy controls, enabling independent security management while participating in cross-organizational data sharing networks. This segmentation resolves the contradiction by allowing security control to be maintained at multiple distributed levels rather than requiring a single centralized authority that restricts data sharing.

The patent introduces privacy-preserving computation technologies and trusted execution environments as intermediaries between data holders and data users. These intermediaries enable data sharing while maintaining security controls through cryptographic proofs, secure enclaves, and policy enforcement mechanisms that prevent direct access to sensitive data. This intermediary layer resolves the contradiction by facilitating data exchange without compromising security control.

The patent divides centralized trust management into distributed trust anchors across multiple organizations. Each trust anchor is responsible for specific policy domains, eliminating the need for a single complex centralized authority. This segmentation reduces system complexity by distributing trust management functions while maintaining policy enforcement capability through coordinated verification across the network.

The patent enables organizations to self-manage their own trust anchors and policy enforcement mechanisms. Each organization can independently configure and maintain its security policies without requiring centralized configuration management. This self-service approach reduces system complexity by eliminating centralized configuration overhead while maintaining policy enforcement capability through decentralized verification.

The patent introduces privacy-preserving computation intermediaries that enable data aggregation for personalization without exposing raw personal data. These intermediaries use techniques such as homomorphic encryption, secure multi-party computation, and federated learning to aggregate data across organizations while maintaining privacy. This resolves the contradiction by enabling personalization capability through data aggregation while eliminating privacy risk through cryptographic protection.

The patent replaces traditional mechanical data collection and aggregation mechanisms with cryptographic-based privacy-preserving computation. Instead of physically collecting and centralizing data, the system uses mathematical proofs and cryptographic protocols to aggregate information while preserving privacy. This substitution resolves the contradiction by enabling personalization through computational aggregation without the privacy risks associated with data collection.