Anonymous Personalized Recommendation via Client-Side Fingerprint Matching
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Solution Overview
Problem
Existing recommendation engines require users to share private information, raising concerns about user privacy as more services move to cloud-based applications, and users are increasingly concerned about privacy issues when accessing large amounts of information on the Internet.
Innovation Solution
A method and system for anonymously recommending public entities by generating and comparing server fingerprints and user fingerprints on the client-side, where server fingerprints are sent from the server to the client without transmitting private user information, allowing for personalized recommendations without compromising user privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing recommendation engines are used to provide personalized recommendations, then recommendation accuracy is improved, but user privacy is compromised due to requirement to share private information
Solution Approach 1:
The system segments the recommendation process into two independent parts: server-side fingerprint generation (encoding public entity information) and client-side fingerprint comparison (matching with user profile). This segmentation allows each side to process only the information it needs without transmitting private user data to the server, thus maintaining recommendation accuracy while protecting user privacy.
Solution Approach 2:
The patent introduces fingerprint encoding as an intermediary mechanism between the server and client. The server generates fingerprints of public entities without accessing user private information, and the client generates fingerprints of user profiles locally. These intermediary fingerprints enable comparison and matching without direct transmission of private user data, resolving the privacy-accuracy contradiction.
2Adaptability or versatility
If user information is transmitted to the server for processing, then personalized recommendations are enabled, but data transmission volume increases and privacy exposure risk increases
Solution Approach 1:
The system extracts only the essential identifying features of public entities and encodes them into compact server fingerprints. These condensed fingerprints are transmitted to the client instead of full entity data or user profile data. The extraction of only necessary information reduces data transmission volume while maintaining the capability for personalized recommendations through fingerprint matching.
3Measurement precision
If comprehensive user information is collected and processed, then recommendation precision is improved, but user privacy protection is weakened
Solution Approach 1:
Instead of the traditional approach where the server collects user information and processes recommendations centrally, the patent inverts the architecture: the server only provides public entity fingerprints, and the client performs the matching operation using locally stored user profile fingerprints. This inversion shifts the privacy-sensitive processing to the client side, improving privacy protection while maintaining recommendation precision through local fingerprint comparison.
Data Source
AI summary
A computer-implemented method for an anonymous personalized recommendation is provided. The method includes receiving select server fingerprints selected from server fingerprints based on predetermined metrics. The server fingerprints correspond to a plurality of public entities and each server fingerprint includes encoded information about a corresponding public entity. The method also includes generating a user fingerprint based on user information associated with a user, the user fingerprint comprising encoded user information. The method further includes comparing the user fingerprint with the select server fingerprints to select a server fingerprint for recommendation, generating a recommendation of a public entity corresponding to the server fingerprint for recommendation, and displaying the recommendation. Systems and machine-readable media are also provided.