Privacy Protected Dynamic User Clustering Middleware
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Solution Overview
Problem
Existing methods for privacy preservation in personalized services and data analytics often compromise user privacy due to resource-intensive homomorphic encryption and centralized solutions that can be vulnerable to malicious actors, especially in dynamic environments where user clusters change frequently.
Innovation Solution
Implementing a middleware node system that dynamically clusters users based on common interests, splitting clusters into sub-clusters when the user count exceeds a predetermined threshold to ensure a minimum cluster size, thereby maintaining user privacy and improving recommendation relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If homomorphic encryption is used for privacy preservation in data analytics, then user privacy is protected, but computational resources and processing time are excessively consumed
Solution Approach 1:
The patent extracts the privacy protection mechanism from centralized homomorphic encryption and implements it at the distributed edge devices. Each edge device performs local clustering and filtering operations, removing the need for resource-intensive centralized encryption processing while maintaining privacy guarantees through distributed computation architecture.
Solution Approach 2:
The patent introduces trusted execution environments (TEEs) and secure multi-party computation protocols as intermediary layers between data sources and analytics systems. These intermediaries enable privacy-preserving data sharing and collaborative filtering without requiring full homomorphic encryption, reducing computational overhead while maintaining security guarantees.
2Device complexity
If centralized solutions are used for data analytics, then implementation is simplified, but vulnerability to malicious actors increases
Solution Approach 1:
The patent segments the centralized analytics system into distributed edge computing nodes that perform local clustering and data filtering. This segmentation distributes trust across multiple independent entities, preventing any single malicious actor from compromising the entire system while maintaining implementation simplicity through modular architecture.
Solution Approach 2:
The patent implements preliminary privacy-preserving operations at the edge devices before data is transmitted to centralized analytics platforms. Clustering, filtering, and aggregation are performed in advance, so that even if the centralized system is compromised, the sensitive raw data has already been transformed into anonymized insights that cannot be traced back to individual users.
3Measurement precision
If user clusters are kept large for data analytics, then statistical accuracy improves, but user identification by malicious parties becomes easier
Solution Approach 1:
The patent implements dynamic cluster formation where user groups are continuously reconfigured based on real-time behavior patterns and contextual factors. Clusters are made dynamic through frequent updates and transformations, making it difficult for malicious actors to track or identify specific users over time while maintaining sufficient cluster sizes for statistically accurate analytics.
Solution Approach 2:
The patent applies different privacy protection strategies to different clusters based on their size, sensitivity, and risk profile. High-risk clusters receive enhanced protection through differential privacy mechanisms and noise addition, while lower-risk clusters use lighter protection methods, optimizing the balance between statistical accuracy and privacy protection locally for each cluster.
Data Source
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AI summary
The present subject matter discloses a method for privacy protected dynamic clustering of end users. In one implementation, a number of end users subscribed to a cluster are ascertained, with the cluster being indicative of a group of end users having a common interest. The method further includes splitting the cluster into two or more sub-clusters if the ascertained number of end users is greater than a predetermined explore-splitting threshold and number of end users subscribed to each of the two or more sub-clusters is greater than a predetermined cluster size threshold.