Dynamic De-Identifiers for Privacy in Decentralized Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems fail to maintain high levels of privacy and anonymity in decentralized networks, particularly with distributed ledger technologies, as static identifiers are easily tracked and lead to re-identification, compromising data security and privacy.
Innovation Solution
The implementation of dynamically changing and temporally unique de-identifiers (DDIDs) that associate with data subjects for specific actions, activities, or traits, allowing for controlled and limited sharing of information, enabling dynamic anonymity and preventing data aggregation by third parties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static identifiers are used in decentralized networks, then ease of operation and tracking is improved, but privacy and anonymity are compromised due to easy re-identification
Solution Approach 1:
The patent applies dynamics by transitioning from static identifiers to dynamic de-identifiers (DDIDs) that change over time. Each DDID is temporally unique and associated with specific actions, activities, or traits, making tracking difficult while maintaining operational functionality. The system dynamically generates, assigns, and revokes DDIDs based on temporal contexts, preventing aggregation of data across different time periods.
2Adaptability or versatility
If data is shared openly in decentralized networks, then accessibility and utility are improved, but data security and privacy are compromised due to aggregation risks
Solution Approach 1:
The patent segments data sharing by creating temporally unique de-identifiers associated with specific actions, activities, or traits. Each DDID represents a segmented portion of data that can be shared independently without revealing the complete identity or full data profile. This segmentation prevents third parties from aggregating sufficient information to re-identify data subjects while maintaining data utility for authorized purposes.
Solution Approach 2:
The system introduces DDIDs as intermediaries between data subjects and third parties. Instead of sharing raw personal data, the system shares temporally unique de-identifiers that mediate the information exchange. These intermediaries provide the necessary data for analytics and tracking while preventing direct access to sensitive personal information, thus securing data while maintaining accessibility.
3Loss of information
If identifiers are changed frequently to maintain anonymity, then privacy is improved, but system complexity increases due to dynamic identifier management
Solution Approach 1:
The patent implements periodic action by generating new DDIDs at defined temporal intervals or upon completion of specific actions. The system periodically revokes old DDIDs and issues new ones, creating a rhythmic pattern of identifier replacement. This periodic approach maintains anonymity through frequent changes while managing complexity through predictable, scheduled updates rather than continuous, random changes.
Data Source
AI summary
Systems, computer-readable media, and methods for improving data privacy/anonymity and data value, wherein data related to a data subject can be used and stored, while minimizing re-identification risk by unauthorized parties and enabling data related to the data subject to be disclosed to an authorized party by granting access only to the data relevant to that authorized party's purpose, time period, place, and/or other criterion via the obfuscation of specific data values. The techniques described herein maintain this level of privacy/anonymity, while still empowering Data Subjects, e.g., consumers or customers of such authorized parties, by enabling them to request or specify their desired level of engagement with various business entities. Data Subjects may then receive privacy-respectful, trusted communication, e.g., advertising materials, based on their inclusion in dynamically changing and/or temporally-limited cohorts or microsegments (“MSegs”) of individuals sharing similar characteristics and having a sufficient size to satisfy anonymity, e.g., “k-anonymity,” requirements.