Synthetic User Profiles via Machine Learning for Privacy-Preserving Quotes
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Solution Overview
Problem
Users are hesitant to provide personal data to quote providers due to concerns about data privacy and misuse, leading to inaccurate quotes when fake information is used.
Innovation Solution
Utilizing machine learning models to generate synthetic user profiles that are similar but distinct from real user data, allowing for the acquisition of accurate quotes without disclosing personal information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If users provide fake information to preserve anonymity, then privacy protection is improved, but quote accuracy deteriorates
Solution Approach 1:
A machine learning model acts as an intermediary between the user and the quote provider. The model generates synthetic user profiles that preserve privacy while maintaining quote accuracy, eliminating the need for users to directly provide personal information to quote providers.
Solution Approach 2:
Instead of using real user data or simple fake information, the system creates synthetic copies of user profiles through machine learning. These synthetic profiles replicate the statistical properties and relationships in real data without containing actual personal information, thus maintaining both privacy and accuracy.
2Measurement precision
If users provide real personal data to quote providers, then quote accuracy is improved, but data privacy security deteriorates
Solution Approach 1:
The machine learning model serves as a privacy-preserving intermediary that processes user data locally and generates synthetic profiles. This eliminates the need for users to transmit sensitive personal information to quote providers, thereby maintaining data privacy security while preserving quote accuracy.
Solution Approach 2:
The system extracts only the necessary statistical patterns and relationships from real user data to generate synthetic profiles. Actual personal identifiers and sensitive information are left behind, extracting only what is needed for accurate quoting while removing privacy risks.
3Object-affected harmful factors
If users use fake information to avoid marketing, then privacy protection is improved, but information reliability deteriorates
Solution Approach 1:
The system creates synthetic copies of user profiles that maintain the statistical properties and relationships found in real data. Unlike arbitrary fake information, these synthetic profiles are generated through machine learning to preserve information reliability while protecting privacy.
Solution Approach 2:
The machine learning model transforms real user data into synthetic profiles by changing specific parameters and relationships while maintaining overall data integrity. This ensures that the synthetic information remains reliable for quoting purposes while protecting user privacy.
Data Source
AI summary
Methods, systems, and apparatuses are described herein for using machine learning to generate and use synthetic profiles. A computing device may train a machine learning model to generate synthetic user profiles. The computing device may then use the trained machine learning model to generate a plurality of synthetic user profiles based on real user profile information, provide those synthetic user profiles to a quote provider via an API, then average the quotes received from that provider to determine an expected quote for the real user. The computing device may also collect a plurality of quotes from a quote provider based on synthetic user profiles, then train a second machine learning model to estimate quotes by that provider. That trained second machine learning model may be used to estimate quotes for users, and may be re-trained based on real quotes provided by the quote provider at a later time.