Synthetic Content Mediation for Privacy-Safe Real Content Procurement
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Solution Overview
Problem
Existing technologies face challenges in procuring relevant content items for users while maintaining user privacy by sharing sensitive data with third-party providers.
Innovation Solution
Generating synthetic content items using machine learning models trained on user data to gauge affinity, allowing selective sharing of real user data with third-party providers to procure relevant content items without compromising privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real user data is shared with third-party content providers, then relevant content items can be procured, but user privacy is compromised
Solution Approach 1:
The patent introduces synthetic content items as an intermediary between user data and third-party providers. Instead of directly sharing real user data, the system generates synthetic representations that capture user preferences and behaviors. These synthetic items serve as a mediator that enables content providers to understand user affinity without exposing sensitive personal information, thus resolving the contradiction between content procurement efficiency and privacy protection
Solution Approach 2:
The system creates synthetic copies of user data characteristics through machine learning models. These synthetic content items replicate the essential patterns and preferences of real user data without containing actual personal information. By using these synthetic copies instead of real data, the system maintains content recommendation accuracy while eliminating privacy risks associated with sharing sensitive user information
2Reliability
If user data is shared with third-party providers, then relevant content items can be obtained, but security risks increase
Solution Approach 1:
Synthetic content items function as a secure intermediary layer that prevents direct exposure of real user data to third-party providers. The synthetic representations maintain the statistical and behavioral characteristics needed for content matching while being inherently safer to share, as they cannot be traced back to specific individuals. This intermediary mechanism achieves content relevance without introducing security vulnerabilities
Solution Approach 2:
The patent employs synthetic content items that are essentially disposable and low-risk data objects. These synthetic representations can be freely shared and processed by multiple providers without concern for long-term security implications, as they lack the persistent identifying characteristics of real user data. This approach enables reliable content procurement while minimizing security risks
3Measurement precision
If more user data is shared, then content accuracy improves, but privacy protection decreases
Solution Approach 1:
The system creates synthetic copies that preserve the statistical properties and behavioral patterns of real user data needed for accurate affinity measurement. The machine learning models generate synthetic content items that replicate preference distributions, interaction patterns, and demographic characteristics without containing identifiable personal information. This copying approach maintains measurement precision while eliminating privacy exposure
Solution Approach 2:
The patent transforms real user data parameters into synthetic representations through machine learning transformations. The system changes the parameter space from identifiable personal attributes to abstracted preference vectors and behavioral patterns. This parameter transformation maintains the informational content needed for accurate affinity measurement while removing the privacy-sensitive characteristics
Data Source
AI summary
Systems and methods of procuring real data items based on user affinity gauged via synthetic data items are disclosed. In one embodiment, an exemplary computer-implemented method may comprise: utilizing a trained machine learning model to generate a synthetic data item based on real user data; presenting the synthetic data item to those users; obtaining indications identifying user responses to the synthetic data item; obtaining user-defined control parameters from the users; configuring a user-defined control mechanism to share a portion of the real user data based thereon; obtaining a subset of the real user data based the user-defined control parameters; providing to a particular third-party data source at least one of: data regarding the synthetic data item, the at least one portion of the real user data, and the indications of the users; and then receiving a second real data item from the particular third-party data source.