Geospatial Data Separation Using Dynamic De-Identifiers
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Solution Overview
Problem
Existing systems struggle to balance the need for data privacy and security with the desire for personalized and targeted data usage, particularly in decentralized networks, leading to challenges in maintaining anonymity and accuracy while enabling authorized data extraction.
Innovation Solution
The use of dynamically changing de-identifiers (DDIDs) associated with data subjects for a temporally unique period, allowing controlled and flexible privacy and anonymity, enabling data sharing only when authorized and within specified contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data subjects maintain strong anonymity through de-identification, then privacy is improved, but data accuracy and personalization capability deteriorate
Solution Approach 1:
The system segments data into multiple dimensions (geospatial, temporal, contextual) and applies different levels of de-identification to each dimension. This allows the data to maintain sufficient accuracy for analytics while protecting privacy through selective masking of sensitive attributes.
Solution Approach 2:
The system dynamically adjusts de-identification parameters based on the specific analytics task, data sensitivity level, and trust relationship. This enables optimization of the balance between privacy protection and data accuracy for different use cases rather than applying a fixed level of anonymization.
2Adaptability or versatility
If decentralized networks enable data sharing, then data usage flexibility is improved, but control over personal information deteriorates
Solution Approach 1:
The system introduces a trusted intermediary layer that manages de-identification and re-identification processes in decentralized networks. This intermediary enables flexible data sharing across multiple parties while maintaining centralized control mechanisms for privacy management and auditability.
Solution Approach 2:
The system implements dynamic de-identification where the level and method of anonymization can change based on the data subject's preferences, the context of data usage, and the trust relationship with data recipients. This allows flexible data sharing while maintaining ongoing control.
3Productivity
If geospatial data is collected for analytics, then personalization capability is improved, but privacy risk increases
Solution Approach 1:
The system applies different levels of geospatial de-identification based on the specific location and context. Sensitive locations receive higher levels of protection through aggregation or masking, while less sensitive areas maintain finer granularity for better personalization. This enables location-based analytics while protecting privacy in vulnerable contexts.
Data Source
AI summary
Various systems, computer-readable media, and computer-implemented methods of providing improved data privacy, anonymity and security by enabling subjects to which data pertains to remain “dynamically anonymous,” i.e., anonymous for as long as is desired—and to the extent that is desired—are disclosed herein. Embodiments include systems that create, access, use, store and/or erase data with increased privacy, anonymity, and security—thereby facilitating the availability of more qualified and accurate information. When personal data is authorized by data subjects to be shared with third parties, embodiments described herein may facilitate the sharing of information in a dynamically-controlled manner that also enables the delivery of temporally-, geographically-, and/or purpose-limited information to the receiving party. In one example, the disclosed techniques may be used to functionally separate geospatial information, such that it remains “dynamically anonymous,” i.e., anonymous for as long as is desired—and to the extent or degree that is desired.