Data Anonymity Protection for Public Identity Exposure Risk

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Solution Overview

Problem

Existing data protection methods, such as access control and encryption, are insufficient to prevent the unintended or intentional disclosure of personal and sensitive information in public domains, leading to potential identity exposure and privacy breaches.

Innovation Solution

A computing system that identifies and transforms potentially identifying data into anonymous data using data corrective operations, machine learning, and artificial intelligence to assess and prevent further exposure, offering personalized insights and adjustments to maintain user anonymity.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If data is distributed in public domain for communication or storage, then data accessibility and utility are improved, but user identity exposure and privacy breach risks increase

Engineering Contradiction:

Improvedata accessibilityVSAvoididentity exposure risk

Core Design Contradiction:

ProductivityVSObject-affected harmful factors

Solution Approach 1:

The system performs preliminary identification and anonymization of personally identifiable information (PII) before data is distributed to public domains. By proactively detecting and correcting potentially exposing data elements prior to publication or sharing, the system prevents identity exposure risks while maintaining data accessibility and utility.

Inventive Principle:

Principle #10Preliminary action

2Reliability

If traditional access control and encryption methods are used, then data security is improved, but they fail to prevent unintended disclosure in public domains

Engineering Contradiction:

Improvedata securityVSAvoidprotection coverage

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system introduces an intermediary layer between data and public domains that actively monitors and corrects data elements. This intermediary component identifies and anonymizes PII dynamically, extending protection coverage beyond traditional access control and encryption methods to include unintended disclosure scenarios in public domains.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Object-affected harmful factors

If all personally identifiable information is anonymized, then privacy protection is improved, but data utility and analytical value are reduced

Engineering Contradiction:

Improveprivacy breach riskVSAvoiddata utility

Core Design Contradiction:

Object-affected harmful factorsVSLoss of information

Solution Approach 1:

The system applies selective anonymization only to specific personally identifiable information elements that pose identity exposure risks, while preserving other data elements that maintain analytical value. This localized approach to PII detection and correction protects privacy by targeting only necessary elements for anonymization, thereby minimizing information loss and maintaining data utility.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS12468852B2Data anonymity protector

Publication Date: 2025.11.11 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Embodiments for providing enhanced data anonymity protection by a processor are disclosed. Selected portions of data intended for distribution in a communication channel or currently distributed on one or more data sources having a potential for revealing identify of a user in a public domain (and/or private domain) may be identified, where an assessment is provided indicating a current status of an amount of data currently exposing the identity of the user in the public domain. The selected portions of the data may be transformed into anonymous data by applying a one or more data corrective operations to prevent further exposure of the identity of the user into the public domain.