Synthetic Identity Publishing for Online Privacy Obfuscation
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Solution Overview
Problem
The increasing availability of personal data on the internet poses a threat to user privacy, as it can be unknowingly accessed by the public or collected without consent, leading to concerns over reputation and security.
Innovation Solution
A machine learning model is trained to generate synthetic personal data, which is then disseminated to public sites, obfuscating the user's real data and making it harder to discern or authenticate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If personal data is published online for public presence, then online visibility and social connectivity are improved, but user privacy and security deteriorate
Solution Approach 1:
The system creates synthetic copies of personal data that mimic the statistical properties and patterns of real data without containing actual personally identifiable information. These synthetic copies are published online instead of real data, maintaining online visibility while protecting privacy.
Solution Approach 2:
A machine learning model acts as an intermediary between the user's real personal data and the public online environment. The model transforms real data into synthetic data before publication, serving as a protective layer that prevents direct exposure of sensitive information while maintaining necessary online presence.
2Object-affected harmful factors
If synthetic personal data is generated and published to obfuscate real data, then user privacy is improved, but data accuracy and authenticity worsen
Solution Approach 1:
The synthetic data maintains local statistical properties and patterns that are characteristic of real data at specific locations in the data distribution, while globally differing from any specific individual's real data. This allows the data to appear authentic for statistical purposes while protecting individual privacy.
Solution Approach 2:
The machine learning model transforms the parameters of personal data by generating synthetic versions that preserve statistical distributions, correlations, and patterns while changing specific individual identifiers and unique characteristics, thereby maintaining data utility for analysis while protecting privacy.
Data Source
AI summary
Aspects described herein may obfuscate publicly available personal information by publishing generated synthetic user personal data to one or more sites. By publishing the generated synthetic personal data to one or more sites, a user's actual personal data will be more difficult to discern and/or detect, resulting in greater privacy, security, and/or control of personal information. A machine learning model may be trained to generate synthetic personal data based on verified personal data and/or training datasets. Upon receiving a request from a user, the machine learning model may generate synthetic personal data, which appears similar to the publicly available information but includes false and/or inaccurate data and/or information about the user. The generated synthetic personal data and/or information may be disseminated to a plurality of sites to hide, or otherwise obfuscate, the user's actual data and/or information.