Data Privacy Enforcer Automates Personal Data Removal
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Solution Overview
Problem
Individuals face difficulties in protecting their personal data from being shared publicly or used by entities without their consent, due to disparate and complex data protection policies across various applications, making it cumbersome to opt-out of data collection and usage, especially with the increasing volume of data collected by businesses.
Innovation Solution
A data privacy enforcer system that includes an opt-out application which identifies user applications sharing personal information, directs users to privacy settings, and utilizes machine learning to automate the process of removing personal data from public sites, ensuring privacy preferences are met across all relevant platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If businesses collect and share data across multiple platforms, then data utility and business value improve, but individual privacy control and data security deteriorate
Solution Approach 1:
The patent introduces a data trust as an intermediary entity that mediates between data generators (individuals) and data consumers (businesses). The data trust holds and manages personal data on behalf of individuals, controlling access and sharing through cryptographic mechanisms. This intermediary structure enables data utility for businesses while maintaining individual privacy control, as the trust can selectively disclose data without exposing raw personal information.
Solution Approach 2:
The patent transforms personal data from its original form into cryptographic representations (hashes, encrypted tokens) that change the parameter state of the data. Data is converted into mathematical proofs and cryptographic credentials that preserve utility for verification purposes while eliminating direct identifiability. This parameter transformation allows businesses to utilize data for analytics and verification without accessing sensitive personal information.
2Reliability
If individuals manually manage privacy settings across multiple applications, then data protection accuracy improves, but user effort and time consumption worsen
Solution Approach 1:
The patent implements self-service mechanisms where the data trust automatically manages privacy controls without requiring continuous user intervention. Once individuals register their data with the trust, the system autonomously handles data sharing permissions, access requests, and compliance with privacy regulations. The cryptographic infrastructure enables automatic verification and enforcement of privacy preferences across multiple platforms without user involvement in each interaction.
Solution Approach 2:
The data trust serves as a universal privacy management system that works across multiple applications, platforms, and data types simultaneously. A single registration with the data trust provides comprehensive privacy protection across diverse services, eliminating the need for individuals to configure separate privacy settings for each application. The trust's cryptographic protocols are platform-agnostic and can enforce privacy controls universally.
3Productivity
If businesses share personal information with third parties, then marketing effectiveness and business intelligence improve, but data exposure risk and compliance complexity worsen
Solution Approach 1:
The patent extracts identifying information from personal data through cryptographic hashing and tokenization. Businesses receive and utilize extracted features, statistical aggregates, and cryptographic proofs that contain marketing intelligence without containing direct personal identifiers. This extraction process enables effective marketing analytics while removing the complexity of managing direct personal information sharing and associated compliance requirements.
Solution Approach 2:
The data trust acts as an intermediary that handles all third-party data sharing transactions. Instead of businesses directly sharing personal information with multiple third parties, the trust mediates these interactions by verifying requests, enforcing privacy permissions, and providing controlled access. This intermediary structure simplifies compliance by centralizing privacy management and automatically ensuring regulatory requirements are met across all data sharing relationships.
Data Source
AI summary
Data privacy enforcers may include providing an opt-out application that identifies user applications that have an individual's personal data and directs the individual to the privacy page for the user applications. The individual may input personal information into the opt-out application that the individual wishes to remain private. The opt-out application may search the internet for the individual's personal information and may provide a list of applications that publicly share the individual's personal information. The individual may select one of the user applications. Upon selection, the opt-out application may direct the individual, in the opt-out application, to the privacy page for the user application. The opt-out application may walk the individual through the options and setting the individual's privacy level for the user application to the individual's preference. The process may iterate through additional user applications identified by the opt-out application as having the individual's information.