In-Memory Data Cleansing With Tag-Based Real-Time Redaction
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Solution Overview
Problem
Compliance with regulations such as HIPAA and GDPR, along with public uneasiness regarding data sharing, increases costs and overheads in using sensitive data, hindering its beneficial use and requiring costly pre-redaction of records.
Innovation Solution
A system for on-demand data cleansing that operates on data in volatile memory, using a cleansing module with tag-operation pairs to modify data fields in real-time, adapting to policies and regulations without reprocessing, and providing high throughput streaming of cleansed data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is redacted before sharing to comply with regulations, then data protection and compliance are improved, but processing time and costs increase
Solution Approach 1:
The system performs preliminary tagging of data fields with metadata identifiers before the actual redaction process. This preliminary action allows the redaction engine to quickly identify and process only the relevant sensitive fields, rather than examining entire records, thereby reducing processing time while maintaining compliance
Solution Approach 2:
The patent segments data records into discrete fields with associated tags, allowing selective redaction of only the sensitive portions identified by specific tags. This segmentation enables parallel processing of multiple fields and avoids unnecessary processing of non-sensitive data, reducing overall processing time
2Reliability
If comprehensive data redaction is performed to ensure compliance, then data security is improved, but data utility for research and monetization deteriorates
Solution Approach 1:
The system applies different redaction levels to different data fields based on their sensitivity and associated tags. Rather than uniformly redacting entire records, the system selectively applies redaction only to specific fields that require protection, preserving the utility of non-sensitive data for research and monetization while maintaining security
Solution Approach 2:
The patent implements variable redaction parameters through configurable tag-operation pairs that allow dynamic adjustment of redaction intensity. This enables the system to balance security and utility by adjusting the degree of redaction based on compliance requirements and data sensitivity, rather than applying fixed comprehensive redaction
3Manufacturing precision
If manual review and redaction processes are used to ensure compliance, then accuracy of compliance is improved, but processing speed and productivity deteriorate
Solution Approach 1:
The system automatically identifies sensitive data fields using pre-defined tags and metadata, eliminating the need for manual review of each field. The tagging system enables self-service identification of sensitive information, allowing automated redaction while maintaining compliance accuracy through rule-based logic
Solution Approach 2:
The patent replaces manual mechanical review processes with automated computational systems that use tag-based identification and rule-based redaction logic. This substitution maintains compliance accuracy through systematic application of regulations while dramatically increasing processing speed
4Reliability
If extensive redaction is performed to protect sensitive information, then privacy protection is improved, but overhead costs and complexity increase
Solution Approach 1:
The system uses a universal tagging system that can identify multiple types of sensitive information (PII, PHI, financial data) with a single standardized approach. This universal tag-based methodology simplifies the system architecture by providing a single entry point for compliance configuration, reducing overall system complexity while maintaining comprehensive privacy protection
Data Source
AI summary
A system and method for on-demand data cleansing is disclosed. The system includes a processor and a volatile memory including a data object having a plurality of data fields, each field having a tag and a value. The system also includes a cleansing module stored in the volatile memory and executed by the processor. The cleansing module includes a library having a plurality of tag-operation pairs. The cleansing module is configured to receive a pointer locating the data object within volatile memory, and further configured to, for each data field, look up the tag of the data field among the library tags, and execute the operation paired with the matching library tag on the value of the field, modifying the value of the data field while it is stored in volatile memory. The cleansing module is configured to execute the operation in near real-time, and at runtime.