Data Correspondence Confidence Scoring with Privacy Policies
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Solution Overview
Problem
Current data mining techniques lack effective methods to determine whether data corresponds to a specific person, especially in large datasets, and fail to protect user privacy while providing accurate information.
Innovation Solution
A system and method that involves receiving and storing observations of a person's data, calculating a confidence score based on these observations to determine data correspondence, and implementing privacy policies to ensure secure data usage, using a repository to store and analyze messages and phone numbers, and applying statistical analysis to prioritize and rank information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data mining techniques analyze large datasets to extract patterns, then information extraction capability is improved, but the ability to determine data correspondence to specific persons deteriorates
Solution Approach 1:
The patent segments the large dataset into individual observation units, each representing a specific data point from a message (e.g., phone number, email address). Each observation is evaluated independently against the person's known data, allowing precise correspondence determination while still processing large datasets systematically.
Solution Approach 2:
The patent introduces an intermediary confidence score that mediates between the extracted data and the person's known information. This confidence score serves as a bridge, quantifying the likelihood that extracted data corresponds to the specific person, thereby resolving the contradiction between broad data extraction and precise correspondence determination.
2Measurement precision
If observations are stored in a repository for analysis, then data accuracy is improved, but user privacy protection deteriorates
Solution Approach 1:
The patent applies local quality by implementing differential privacy protections tailored to specific data fields and observation types. Different privacy protection mechanisms are applied based on the sensitivity and type of each data element, allowing accurate analysis of less sensitive data while providing stronger protection for highly sensitive personal information.
Solution Approach 2:
The patent introduces privacy-preserving intermediaries such as trusted execution environments and secure enclaves that mediate between the repository data and analysis processes. These intermediaries enable accurate data correspondence determination while preventing direct access to raw personal information, thus protecting user privacy.
3Measurement precision
If confidence scores are calculated based on multiple observations, then data correspondence determination is improved, but computational complexity deteriorates
Solution Approach 1:
The patent performs preliminary actions by pre-processing observations to extract and normalize key features before confidence score calculation. Data is pre-aggregated and filtered to identify the most relevant observations, reducing the computational burden of subsequent confidence calculations while maintaining determination accuracy.
Solution Approach 2:
The patent changes parameters by implementing adaptive confidence scoring that adjusts calculation depth based on initial observation quality. When observations clearly indicate correspondence, simplified scoring is used; when observations are ambiguous, more complex analysis is applied, optimizing computational resources while maintaining precision.
Data Source
AI summary
Systems and methods to store data obtained from observations and to determine a correspondence of certain data to a particular person. In one approach, a method includes: receiving or making a plurality of observations for a person (e.g., data extracted from e-mails sent to the person); storing the observations in a repository (e.g., a database on a server); and determining whether data in a first observation of the observations corresponds to the person, wherein the determining is based on the plurality of observations.