Confidential Data Insights via Peer Group Bayesian Smoothing
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Solution Overview
Problem
There is a challenge in collecting and maintaining confidential data, such as salary information, while ensuring its confidentiality and utilizing it for specific purposes, as users are reluctant to share due to privacy concerns and technical challenges in ensuring data security and anonymity.
Innovation Solution
A system is developed that gathers and tracks confidential data securely, using semantic representations to infer insights for organizations with limited data by forming peer organization groups and applying Bayesian smoothing to correct for data sparsity and prevent individual data detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If confidential data is collected and stored for organizational insights, then data security and user privacy are compromised, but without collection, meaningful organizational-level insights cannot be provided
Solution Approach 1:
The patent segments confidential data into individual organizational units, allowing separate handling and analysis of data from each organization. This enables the system to maintain security boundaries while collecting sufficient data across multiple organizations to provide meaningful insights without compromising individual organizational privacy.
Solution Approach 2:
The patent introduces an intermediary processing layer that aggregates and analyzes confidential data without exposing raw individual records. This intermediary mechanism allows organizational insights to be generated while maintaining data security through controlled access and anonymization techniques.
2Loss of information
If confidential data is aggregated at organization level, then meaningful insights can be provided, but data sparsity in small organizations reduces insight accuracy
Solution Approach 1:
The patent merges data from multiple organizations into aggregated cohorts, combining small organizational datasets with larger ones to achieve sufficient statistical significance. This merging approach allows small organizations to benefit from insights that would otherwise require much larger individual datasets.
Solution Approach 2:
The patent applies partial aggregation by selectively combining data from organizations with similar characteristics while maintaining separate analysis for unique cases. This approach provides enough data aggregation to improve measurement precision without over-aggregating and losing organization-specific nuances.
3Loss of information
If peer organization groups are formed using semantic representations, then insights can be provided for organizations with limited data, but system complexity increases
Solution Approach 1:
The patent changes the parameter space by introducing semantic representations (embeddings) that transform organizational attributes into continuous vector spaces. This allows the system to group organizations based on semantic similarity rather than exact attribute matching, enabling insights for organizations with limited data while managing complexity through efficient vector operations.
Data Source
AI summary
In an example embodiment, submitted confidential data of a certain cohort (e.g., title, region, organization) is split into two components covering different portions of the cohort attributes (e.g., a first portion for (title, region)-wise confidential data values and a second portion of organization-wise compensation adjustments). These two portions are then analyzed separately and the inferences from both models are integrated together to obtain predictions for compensation values for the cohort as a whole.