Privacy Policy System Measuring Information Loss via Entropy
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Solution Overview
Problem
Current systems for applying data privacy policies lack efficiency and objectivity, leading to subjective decisions that do not adequately balance privacy and utility, resulting in significant information loss and reduced analytic value of datasets.
Innovation Solution
A system that efficiently samples datasets, determines ridge statistics, measures entropy before and after applying privacy policies, calculates information loss, and applies policies based on quantified impact, enabling objective decisions and minimizing information loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If personal or sensitive data is removed or redacted from data stores, then privacy protection is improved, but analytic value of the dataset is reduced
Solution Approach 1:
The patent introduces an intermediary system that sits between the raw data and the analytics processing. This system applies privacy-preserving transformations (such as differential privacy, generalization, or suppression) to create a modified dataset that protects individual privacy while maintaining sufficient analytic value for organizational purposes. The intermediary enables both privacy protection and analytics by transforming the data rather than simply removing it.
Solution Approach 2:
The patent applies parameter changes to data elements to protect privacy while preserving utility. This includes techniques such as generalizing specific values (e.g., converting exact ages to age ranges, or precise locations to broader geographic areas), adding controlled noise to numerical data, or applying suppression thresholds. These parameter transformations reduce the identifiability of individuals while maintaining the statistical properties needed for analytics.
2Reliability
If data privacy policies are applied to protect sensitive information, then privacy safeguards are improved, but data utility for analysis is reduced
Solution Approach 1:
The patent implements dynamic privacy policies that can be adjusted based on the analytical context, data sensitivity, and organizational needs. Rather than applying static, one-size-fits-all privacy measures, the system dynamically selects and adjusts privacy transformation parameters based on factors such as the type of analysis being performed, the sensitivity of specific data elements, and the required level of privacy protection. This enables flexible balancing of privacy safeguards and data utility.
Solution Approach 2:
The patent applies different privacy protection levels to different data elements or regions within the dataset based on their sensitivity and analytical value. Instead of uniformly applying privacy measures across all data, the system identifies specific data elements that require protection and applies appropriate privacy transformations only to those elements, leaving other data elements in their original form or with minimal transformation. This localized approach preserves data utility while providing targeted privacy protection.
3Ease of operation
If subjective decisions are made about applying privacy policies, then policy application simplicity is improved, but information loss increases due to lack of objectivity
Solution Approach 1:
The patent incorporates feedback mechanisms that automatically evaluate the impact of privacy policy applications on data utility. The system monitors metrics such as information loss, analytic value retention, and privacy protection effectiveness, and uses this feedback to automatically adjust privacy policy parameters or select appropriate policies. This closed-loop approach replaces subjective decision-making with objective, data-driven policy application that minimizes information loss while maintaining privacy safeguards.
Solution Approach 2:
The patent implements self-service automated systems that evaluate and apply privacy policies without requiring subjective human judgment. The system automatically assesses data sensitivity, selects appropriate privacy transformations, applies the policies, and evaluates their impact on data utility. This automation eliminates subjective bias and inconsistency in policy application while maintaining simplicity through automated decision-making based on predefined criteria and objective metrics.
Data Source
AI summary
Systems and methods for obscuring data from a data source include devices and processes that may objectively measure the information loss for the data source that is caused by applying a privacy policy, and may apply a policy to the data source based on the measured information loss. The systems and methods may measure the information loss for a large data source by taking a representative sample from the data source and applying the policy to the sample in order to quantify the information loss. The quantified information loss can be iteratively used to change the policy in order to meet utility and/or privacy goals, and the system can subsequently apply the changed policy to the data source.