Dynamic De-Identifiers for Data Privacy and Value
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Solution Overview
Problem
Current systems fail to effectively balance data privacy and security with the need for personalized offerings and research, as static identifiers can be easily tracked and lead to re-identification, compromising anonymity and security.
Innovation Solution
The use of dynamically changing, temporally unique de-identifiers (DDIDs) that change over time and are reassigned for different purposes or subjects, creating a 'dynamically anonymous' state that prevents data retention and aggregation by third parties, allowing for controlled sharing of information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static identifiers are used for data tracking and personalized offerings, then data value for personalization and research is maximized, but data privacy and security are compromised due to easy tracking and re-identification
Solution Approach 1:
The patent applies dynamics by transitioning from static identifiers to dynamic de-identifiers (DDIDs) that change over time. The DDID system generates temporally unique identifiers that are reassigned periodically, making tracking difficult while preserving data utility for personalization and research purposes.
Solution Approach 2:
The patent changes the parameter of identifier stability by introducing DDIDs with temporal uniqueness. The system modifies the identifier state based on time parameters, creating a dynamic identification system that balances privacy protection with data usability through controlled reassignment cycles.
2Object-affected harmful factors
If dynamically changing de-identifiers are used to maintain privacy, then data security and anonymity are enhanced, but data aggregation and retention by third parties is prevented
Solution Approach 1:
The patent segments the identification process into temporal segments through DDIDs. Each time period has its own identifier, preventing third parties from aggregating data across time while maintaining security. The segmentation breaks the continuity required for data aggregation without compromising individual data point security.
Solution Approach 2:
The DDID system acts as an intermediary between data subjects and data processors. It provides a layer of abstraction that protects privacy while enabling controlled data sharing. The intermediary mechanism allows authorized access to information without exposing identifying details, balancing security with data utility.
3Object-affected harmful factors
If temporally unique identifiers are implemented to prevent re-identification, then anonymity is maintained, but controlled sharing of information becomes more complex
Solution Approach 1:
The patent implements a universal DDID system that serves multiple functions: privacy protection, data tracking within time windows, authorized information sharing, and research enablement. This multi-functional approach reduces the need for separate systems while maintaining anonymity and controlling information flow through a single integrated mechanism.
Data Source
AI summary
Systems, computer-readable media, and methods for improving both data privacy/anonymity and data value, wherein real-world, synthetic, or other data related to a data subject can be used while minimizing re-identification risk by unauthorized parties and enabling data, including quasi-identifiers, related to the data subject to be disclosed to any authorized party by granting access only to the data relevant to that authorized party's purpose, time period, purpose, place and/or other criterion via the required obfuscation of specific data values, e.g., pursuant to the GDPR or HIPAA, by incorporating a given range of those values into a cohort, wherein only the defined cohort values are disclosed to the given authorized party. Privacy policies may include any privacy enhancement techniques (PET), including: data protection, dynamic de-identification, anonymity, pseudonymity, granularization, and/or obscurity policies. Such systems, media and methods may be implemented on both classical and quantum computing devices.