PII Comparison Engine for Dynamic Industry Benchmark Feedback
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Solution Overview
Problem
Existing systems face challenges in efficiently managing and anonymizing personally identifiable information (PII) across disparate customer systems, lacking effective methods to update industry benchmarks dynamically and provide real-time feedback for improved data management.
Innovation Solution
A system comprising a CRM connected to various engines, including an IPII anonymizing engine, continuous IPII destruction engine, bit labeling engine, machine learning engine, and auto-meta comparator feedback engine, which processes and analyzes PII to provide dynamic industry state comparisons and actionable feedback for enhanced data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If PII is anonymized and processed across disparate customer systems, then data privacy and security are improved, but the ability to maintain accurate industry benchmarks and provide real-time feedback deteriorates due to lack of centralized dynamic updates
Solution Approach 1:
The system segments PII processing into multiple specialized engines (anonymizing engine, destruction engine, feature extraction engine) that operate independently but coordinate through a centralized CRM. This allows each component to handle specific aspects of PII management while the centralized system maintains overall coordination for benchmark updates and feedback distribution.
Solution Approach 2:
The system implements continuous feedback loops where the CRM receives anonymized PII data from multiple customer systems, processes it through various engines, generates industry benchmarks and insights, and distributes feedback to all connected systems. This enables dynamic updates of industry state comparisons while maintaining data privacy through anonymization.
2Productivity
If multiple engines process PII data through the CRM, then data management capability and analytical insight are improved, but system complexity increases
Solution Approach 1:
The CRM serves as a universal central hub that coordinates multiple specialized engines (anonymizing, destruction, feature extraction, bit labeling, machine learning, feedback). Each engine is designed to perform its specific function while interfacing with the same standardized CRM architecture, reducing overall system complexity through unified coordination.
Solution Approach 2:
The system merges multiple PII processing functions into a single integrated CRM platform rather than distributing them across separate systems. This consolidation allows centralized management of PII data flows, coordinated processing through multiple engines, and unified distribution of benchmarks and feedback, improving data management capability while containing complexity through centralization.
3Manufacturing precision
If continuous PII destruction and feature extraction are performed in real-time, then data quality and actionable insights are improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary anonymization and feature extraction on PII data as it enters the CRM, before comprehensive analysis and benchmarking. This preliminary processing prepares data in advance for subsequent analytical operations, ensuring data quality is maintained while reducing the time required for later processing stages.
Solution Approach 2:
The system implements continuous processing pipelines where PII data flows through multiple engines (anonymizing, destruction, feature extraction, machine learning) without interruption. This continuous action ensures data quality is maintained throughout processing while optimizing computational resource utilization through steady-state operation rather than batch processing.
Data Source
AI summary
Among other techniques, techniques for providing feedback associated with personally identifiable information (PII) to a customer that is a member of an industry are described. The feedback includes a comparison with dynamic industry state. A system utilizing these techniques can include a continuous initially personally identifiable information (IPII) destruction engine; an IPII feature extraction engine coupled to the continuous IPII destruction engine; a bit labeling engine coupled to the continuous IPII destruction engine; a classified and interpreted IPII attribute instance bits datastore coupled to the bit labeling engine; and an auto-meta comparator feedback engine coupled to the classified and interpreted IPII attribute instance bits datastore. A method utilizing these techniques can include subdividing into affixes initially personally identifiable information (IPII) attribute instances of a first customer of a plurality of customers that are part of an industry; outputting bits corresponding to the IPII attribute instances; attaching labels to the bits, which are stored with metadata; and providing feedback to the first customer for the IPII attribute instances along with a dynamic auto-metadata comparison relative to dynamic industry state of the industry, wherein IPII attribute instances of the plurality of customers are used to update the dynamic industry state.