Role-Based Data Anonymization System
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Solution Overview
Problem
Current data anonymization systems lack flexibility in addressing the privacy-utility tradeoff and do not account for the role or identity of data requesters, limiting their ability to adjust the level of anonymity and data quality according to specific requirements, such as legal compliance and application-specific needs.
Innovation Solution
A data anonymization system that applies a base anonymization function and a custom anonymization function based on the role of the data requester, allowing for various anonymization techniques like encryption, k-anonymity, and data masking, to produce custom anonymized data that meets the privacy and utility requirements of different users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data masking or generalization is applied to anonymize subscriber data, then privacy protection is improved, but data quality and utility deteriorate
Solution Approach 1:
The system dynamically adjusts the anonymization degree based on the requester's role and specific needs. Different anonymization functions (base anonymization, custom anonymization, de-anonymization) are applied dynamically rather than using a fixed approach, allowing the system to optimize the balance between privacy protection and data utility for each specific case
Solution Approach 2:
The system applies different levels and types of anonymization to different data fields or subsets of data based on the requester's role. For example, certain sensitive fields may be heavily anonymized while other fields retain more detail, allowing each part of the data to have the appropriate level of quality for its intended use
2Device complexity
If a single anonymization function is used for all requesters, then system complexity is reduced, but adaptability to different privacy and utility requirements deteriorates
Solution Approach 1:
The anonymization system is segmented into multiple distinct functions: base anonymization function, custom anonymization function, and de-anonymization function. Each function serves a specific purpose and can be selected based on the requester's role, making the system adaptable without becoming unmanageably complex
Solution Approach 2:
The system is designed to serve multiple functions through a unified architecture that can apply different anonymization strategies based on requester role. The same system infrastructure supports base anonymization for general requests, custom anonymization for role-specific requests, and de-anonymization for authorized users, making it universally applicable to various privacy-utility scenarios
3Reliability
If irreversible one-way data transformation is applied, then privacy protection is improved, but ability to obtain original data or improve data quality deteriorates
Solution Approach 1:
The system dynamically selects between irreversible anonymization and reversible de-anonymization based on the requester's authenticated role. For regular users, irreversible base anonymization is applied to ensure privacy, while for authorized users with appropriate roles, reversible custom anonymization or de-anonymization is applied to allow retrieval of original data when needed
Data Source
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AI summary
A data anonymization system provides role-based anonymization for data requesters. The system applies a base anonymization function to subscriber data related to at least one subscriber of a service provider to produce base anonymized subscriber data. Upon receiving a request for the subscriber data from a data requester, a role assigned to that data requester is determined to identify a custom anonymization function to be applied to the subscriber data in order to produce custom anonymized subscriber data for the data requester.