Decentralized Privacy Protection System for Recommendation Services
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Solution Overview
Problem
Existing solutions for protecting user privacy in recommendation services either rely on untrustworthy centralized intermediaries, fail to support thin clients, or provide limited protection due to encrypted/unencrypted data requirements, leading to vulnerabilities in personal information management.
Innovation Solution
A decentralized privacy protection system using a distributed collection of non-colluding intermediary nodes that support thin clients and allow unencrypted communication, employing a network anonymization layer with DHT routing and encryption mechanisms to protect user consumption data and maintain anonymity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a centralized intermediary is used to protect user privacy in recommendation services, then user privacy can be protected through centralized control, but the system becomes vulnerable to trust issues and single points of failure
Solution Approach 1:
The patent divides the centralized intermediary into multiple distributed intermediary nodes that operate independently. Each node processes user consumption data anonymously without a central authority, eliminating single points of failure and trust vulnerabilities while maintaining privacy protection capabilities across the recommendation system
Solution Approach 2:
The patent introduces encrypted communication channels and anonymization layers as intermediaries between users and recommendation services. These intermediaries protect user data during transmission and processing without requiring trust in the underlying infrastructure, enabling secure privacy-protected recommendations
2Reliability
If encrypted communication is used to protect user data, then privacy protection is improved, but thin clients and diverse computing devices cannot be supported due to processing requirements
Solution Approach 1:
The patent enables thin clients and diverse computing devices to perform local encryption and anonymization of user consumption data before transmission. This self-service approach allows lightweight devices to protect their own data without requiring powerful centralized processing, expanding compatibility across different client types while maintaining strong privacy protection
Solution Approach 2:
The patent performs encryption and data anonymization at the source (user device) before data leaves the client. This preliminary action ensures data is protected during transmission and storage without requiring continuous heavy processing at intermediary nodes or servers, enabling support for resource-constrained thin clients
3Productivity
If user consumption data is collected for recommendation services, then personalized recommendations can be provided, but user privacy and anonymity are compromised
Solution Approach 1:
The patent extracts personally identifiable information from user consumption data through anonymization processes. The system retains and processes only the anonymized consumption patterns needed for generating personalized recommendations, while removing or masking any data that could identify specific users, thus enabling both service quality and privacy protection
Solution Approach 2:
The patent transforms user data from identifiable dimensions to anonymous statistical dimensions. By converting individual user consumption records into aggregated, anonymized patterns across multiple users, the system enables personalized recommendations based on group behavior patterns without exposing individual user identities or specific consumption details
Data Source
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AI summary
Method(s) and system(s) for providing privacy to personal information of end users while utilizing recommendation services and personalized content are described. According to the present subject matter, the system(s) implement the described method(s) for providing privacy to personal information of end users. The method for privacy protection includes receiving user consumption data associated with one or more end users where the user consumption data comprises at least a slice of interest profile and associated interest group id of the slice of interest profile; and where the user consumption data is received through a network anonymization layer. The method may further include routing the user consumption data to an interest group aggregator node from amongst a plurality of intermediary nodes based on distributed hash table routing mechanism, and wherein the interest group aggregator node is associated with the interest group id present in the user consumption data.