Decentralized Latent Semantic Indexing via Distributed Average Consensus
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Solution Overview
Problem
Current distributed computing systems face challenges in verifying the contribution of computing devices to calculations, leading to potential compensation without actual work being performed, and centralized search engines require data exposure for indexing and user behavior tracking, compromising privacy.
Innovation Solution
Implementing a distributed average consensus (DAC) algorithm that allows computing devices to exchange results peer-to-peer, confirming participation through consensus, and using a cooperative subspace approach with random sampling for latent semantic indexing, enabling decentralized data analysis without exposing individual document contents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a centralized server is used to index documents and collect user behavior data, then search functionality is improved, but user privacy is compromised due to data exposure and centralized collection
Solution Approach 1:
The patent divides the centralized search engine into distributed peer-to-peer computing nodes. Each node independently performs local indexing and search operations on its own data, eliminating the need for a central server to collect and store all user data. This segmentation maintains search functionality while preserving user privacy through decentralized processing.
2Productivity
If distributed computing devices are used to perform computations, then computing resources are utilized more efficiently, but verification of actual contribution becomes difficult
Solution Approach 1:
The patent implements a feedback mechanism where computing devices exchange verification data with peer nodes to confirm actual contribution to computations. Each device provides proof of work or computation results to neighboring nodes, which verify and aggregate these contributions. This feedback loop ensures reliable verification of contribution while maintaining efficient distributed computing resource utilization.
Data Source
AI summary
A distributed computing device calculates word counts for each of a set of documents. The word counts are represented as values, each representing a number of times a corresponding word appears in one of the set of documents. The distributed computing device randomly samples the word counts to calculate sampled word counts. The distributed computing device and additional distributed computing devices iteratively execute a process to determine a consensus result for the sampled word counts based on the sampled word counts and additional sampled word counts calculated by the additional distributed computing devices. The distributed computing device determines a latent semantic index (LSI) subspace based on the consensus result for the sampled word count and reflecting contents of the set and additional sets of documents. The distributed computing device projects a document into the LSI subspace to determine the latent semantic content of the document.