Personal Data Management via Discontinuity Engine and Pervasive Cryptography
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Solution Overview
Problem
Existing systems for managing personal medical data fail to ensure comprehensive privacy protection, particularly against hacking, as they either create a single point of failure due to inadequate security or limit data sharing and analysis by not utilizing an organic approach to anonymization and encryption.
Innovation Solution
The system employs pervasive cryptography to separate information domains, encrypting and anonymizing data, and using a Discontinuity Engine Interface to manage communications, ensuring that only authorized services access encrypted data, while the user maintains control over their data through contract-based actions and temporary profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If electronic data are made accessible to multiple parties for analysis and sharing, then data utility and analysis capability are improved, but privacy protection and security are worsened
Solution Approach 1:
The system segments personal data into multiple independent components (direct identifiers, indirect identifiers, sensitive attributes) that are stored and processed separately. This allows different services to access only the components they need for their specific functions, enabling data sharing while maintaining privacy through the separation of identification and sensitive information.
Solution Approach 2:
The patent introduces intermediary components including anonymization services that transform identifiable data into anonymous data, encryption services that protect data during storage and transmission, and a hub service that coordinates access between multiple parties. These intermediaries enable secure data sharing by mediating between data holders and data users without exposing raw personal information.
2Ease of operation
If a centralized repository is created to combine multiple services, then data accessibility is improved, but security vulnerabilities and single point of failure are worsened
Solution Approach 1:
Instead of a single centralized repository, the system divides data storage across multiple independent services (archive service, analysis service, hub service). Each service stores only the data components it needs to fulfill its specific function, eliminating the single point of failure while maintaining coordinated access through the hub service.
Solution Approach 2:
Different services are assigned different security requirements and access controls based on their specific functions. The archive service implements strict access controls for stored data, the analysis service processes only anonymized data with appropriate cryptographic protection, and the hub service manages coordination without accessing sensitive information. This localized security approach allows each service to be optimized for its specific security needs.
3Object-affected harmful factors
If local archiving is implemented to protect against online hacking, then security against remote attacks is improved, but data sharing capability and analysis capability are worsened
Solution Approach 1:
The hub service acts as an intermediary that enables secure data sharing between local archives and external services. It manages cryptographic keys, coordinates access requests, and ensures that data can be shared with authorized parties while maintaining local storage security. This intermediary layer preserves the security benefits of local archiving while enabling the data sharing capabilities of online systems.
4Object-affected harmful factors
If anonymization is applied to protect user identity, then privacy protection is improved, but data analysis precision and identification capability are worsened
Solution Approach 1:
The system segments data into different levels of anonymity. Direct identifiers are completely removed or encrypted, while indirect identifiers and sensitive attributes are retained in anonymized form that preserves statistical and analytical value. This segmentation allows analysis services to work with data that protects user identity while maintaining sufficient precision for medical and research analysis.
Data Source
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AI summary
A system (1) for the management of personal data (3) relative to a user by maintaining personal privacy, comprising a Discontinuity Engine Interface (4) configured for receiving identification data (2) of the user and receiving encrypted personal data (3) of the user. The system uses separation of information domains to achieve the maximum privacy, different system components have only parts of the information as they manage information or encrypted, obfuscated or anonymous data also in combination. This separation between services and the way information are accessed permit to guarantee the maximum privacy against direct and indirect identification of the client. This level of security is permitted by pervasive cryptography starting from encapsulating data from the originator: client or analysis laboratory.