Trusted Function Handler for Privacy-Preserving Recommendations
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Solution Overview
Problem
Recommendation systems require knowledge of user preferences, which can be a privacy concern for users hesitant to share their rating information with third parties.
Innovation Solution
A system comprising a client device or service provider server with a trusted function handler module and recommender module that transforms user rating information using a selected linear transformation, ensuring the privacy of user data by keeping the original information unknown to the recommender module.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user rating information is shared with the recommender system, then recommendation accuracy is improved, but user privacy is compromised
Solution Approach 1:
A trusted function handler acts as an intermediary between the user and the recommender system. This handler applies linear transformations to user rating information before transmission, ensuring that the recommender receives transformed data that preserves recommendation accuracy while preventing direct access to original private user information.
Solution Approach 2:
The system changes the parameters of user rating information by applying linear transformations (such as rotation matrices) to the data. This transformation modifies the representation of user preferences while preserving the underlying patterns needed for accurate recommendations, thereby decoupling accuracy from direct data exposure.
2Object-affected harmful factors
If linear transformation is applied to user rating information, then user privacy is protected, but system complexity increases
Solution Approach 1:
The system is segmented into distinct functional modules: a trusted function handler that applies transformations, a recommender that processes transformed data, and a mapping module that handles inverse transformations. This segmentation isolates the complexity of linear transformations to specific components while keeping the overall system manageable through clear separation of concerns.
3Object-affected harmful factors
If transformed user rating information is used for recommendation, then privacy is maintained, but information loss may occur
Solution Approach 1:
The system implements a feedback mechanism where the trusted function handler provides transformation mappings and the recommender system uses inverse transformations to recover meaningful information. This feedback loop ensures that while privacy is maintained through transformation, the essential user preference information is preserved and can be accurately reconstructed for recommendation purposes.
Data Source
AI summary
The invention provides an improved recommender system that includes a client device or service provider server, a trusted function handler module and a recommender module. The recommender system functions to protect the privacy of user rating information maintained by the node (i.e., client device/server) by having the node transform the user rating information using a specific function selected by the function handler and then provide the transformed user rating information to the recommender module. In this way, privacy of the user rating information is maintained because the original user rating information will be unknown to a recommender module.