Adaptive Data De-Identification Rules for Evolving Streams
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Solution Overview
Problem
Static data de-identification rules become outdated due to changing data and knowledge, making them susceptible to re-identification and privacy attacks, as they are not dynamically adapted to evolving conditions.
Innovation Solution
A system that periodically monitors datasets and dynamically changes data de-identification rulesets using machine learning to maintain privacy protection, adapting rules in real-time to ensure compliance with privacy requirements and legal frameworks like HIPAA Safe Harbor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static data de-identification rules are used, then initial privacy protection is achieved, but the protection becomes outdated and vulnerable to re-identification attacks over time
Solution Approach 1:
The patent implements dynamic data de-identification by continuously monitoring data streams and automatically adjusting de-identification rules in real-time. The system transitions from static, pre-defined rules to dynamic rules that adapt to changing data characteristics, population density, and re-identification risks, ensuring ongoing privacy protection effectiveness.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously monitoring data streams, evaluating the effectiveness of current de-identification rules, and using this information to adjust rules dynamically. The monitoring component detects changes in data characteristics and feeds this information back to the rule adjustment mechanism, creating a closed-loop system that maintains privacy protection adaptability.
2Reliability
If de-identification rules are frequently updated to maintain privacy protection, then privacy security is improved, but system complexity and computational resources increase
Solution Approach 1:
The system implements self-service by automatically monitoring data characteristics, evaluating rule effectiveness, and adjusting de-identification rules without requiring manual intervention. The automated rule adjustment mechanism uses pre-defined criteria and algorithms to dynamically modify rules, reducing operational complexity while maintaining high privacy security through continuous adaptation.
3Reliability
If continuous monitoring and dynamic rule adjustment are implemented, then privacy protection is maintained, but processing time and computational resources increase
Solution Approach 1:
The system implements periodic monitoring and evaluation of de-identification rules at specified intervals rather than continuously for every data point. This periodic action maintains privacy protection effectiveness while reducing computational overhead and preserving data processing efficiency by adjusting the frequency of rule evaluations based on data stream characteristics.
Data Source
AI summary
A system dynamically changes a data de-identification ruleset applied to a dataset for de-identifying data and comprises at least one processor. The system periodically monitors a dataset derived from data that is de-identified according to a data de-identification ruleset under a set of conditions. The set of conditions for the data de-identification ruleset is evaluated with respect to the monitored data to determine applicability of the data de-identification. One or more rules of the data de-identification ruleset are dynamically changed in response to the evaluation indicating one or more conditions of the set of conditions for the data de-identification ruleset are no longer satisfied. Embodiments of the present invention may further include a method and computer program product for dynamically changing a data de-identification ruleset applied to a dataset for de-identifying data in substantially the same manner described above.