Network Agent Nodes Privacy-Preserving Statistical Profile Generation
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Solution Overview
Problem
Existing methods for gathering consumer information and statistics often compromise user privacy, as sensitive data must be transmitted to centralized systems, making individuals reluctant to share private information.
Innovation Solution
A method and system for generating process requests that discover and access local information from agent nodes in a network, classify data using learning algorithms, and create cumulative profiles that exclude personally identifying information, ensuring privacy by incorporating noisy data and using a peer-to-peer network with user agent applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If user information is transmitted to centralized systems for processing, then statistical analysis and business intelligence can be obtained, but user privacy and data security are compromised
Solution Approach 1:
The patent implements local processing at agent nodes where data remains, rather than centralizing it. Each agent node processes information locally and contributes only aggregated statistical results to the cumulative profile, maintaining local data quality and privacy while achieving global analytical objectives.
Solution Approach 2:
The patent introduces an intermediary cumulative profile that acts as a mediator between individual agent nodes and external systems. This cumulative profile contains only aggregated statistical information without personally identifying details, serving as a privacy-protecting interface that enables business intelligence while preventing direct access to sensitive user data.
2Object-affected harmful factors
If encrypted or scrambled information is transmitted to central locations, then privacy protection is improved, but data transmission and processing complexity increase
Solution Approach 1:
Instead of encrypting data before transmission to centralized systems, the patent inverts the approach by processing data locally at agent nodes and transmitting only aggregated statistical results. This eliminates the need for complex encryption and decryption infrastructure while maintaining privacy protection.
Solution Approach 2:
The patent extracts only the essential statistical information from local agent nodes, leaving sensitive detailed data behind at each node. This extraction approach transmits minimal necessary information to the cumulative profile, reducing transmission complexity and eliminating the need for complex privacy-preserving transmission mechanisms.
3Loss of information
If sensitive consumer data is collected for behavioral analysis, then business intelligence and marketing strategies are improved, but consumer reluctance and privacy concerns increase
Solution Approach 1:
The patent enables each agent node to maintain control over its own data locally, processing information in place rather than requiring users to send sensitive data elsewhere. This local processing approach builds user trust and participation while still enabling comprehensive behavioral analysis through aggregated statistical profiles.
Solution Approach 2:
The system allows agent nodes to autonomously contribute their own statistical information to the cumulative profile without requiring active user intervention or data transmission. This self-service mechanism reduces user burden and privacy concerns while continuously gathering behavioral information for business intelligence.
Data Source
AI summary
A method, system, and computer program product for gathering information and statistics from a community of agent nodes in a network is provided. The method includes generating a process request. The process request includes a rule for discovering each of the agent nodes in the community and a rule for accessing local information stored on each of the agent nodes. The local information represents activities conducted by end users at each of the agent nodes. The process request also includes a set of rules for classifying data comprising the local information and developing a cumulative profile resulting from the classifying. The cumulative profile includes generic information that is descriptive of collective activities conducted by the end users and is absent information that personally identifies any of the end users.