Data Storage Nodes Collaborative Statistical Analysis
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Solution Overview
Problem
Existing data statistical analysis methods face a privacy data leakage issue where central nodes receive and analyze sensitive data from multiple storage nodes, compromising data security.
Innovation Solution
Implementing a method where data storage nodes encrypt data using secret sharing algorithms, distribute encrypted data to other nodes, and process it locally before sending aggregated results to a proxy node for analysis, preventing the central node from accessing individual data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If all data storage nodes send their stored data to a central node for statistical analysis, then the central node can complete data analysis, but privacy data leakage occurs
Solution Approach 1:
The patent segments the original data into multiple encrypted fragments using secret sharing algorithms. Each data storage node holds only a portion of the encrypted data, and no single node possesses the complete information. This segmentation prevents any individual node from accessing full privacy-sensitive data while still enabling collective statistical analysis through aggregation of encrypted fragments.
Solution Approach 2:
The patent introduces encrypted data as an intermediary between the original data and the analysis process. Instead of transmitting plaintext data to a central node, the system uses encrypted data fragments that serve as intermediaries. These encrypted intermediaries allow statistical analysis to proceed while preventing direct access to the underlying sensitive information, thus resolving the contradiction between analysis efficiency and privacy protection.
2Object-affected harmful factors
If data is encrypted and distributed to multiple nodes, then data security is improved, but processing complexity increases
Solution Approach 1:
The patent changes the state of data from plaintext to encrypted form using secret sharing algorithms. This parameter change (encryption) transforms the data into a state that provides security while maintaining usability for statistical analysis. The encrypted fragments can still be aggregated and processed, but their cryptographic transformation ensures security, thus improving data security without fundamentally breaking the processing workflow.
Data Source
AI summary
Data storage nodes that participate in a requested data statistical analysis as participant data storage nodes are determined and divided into a plurality of node sets. Data stored in each participant data storage node associated with a particular node set is encrypted, where the encrypted data is divided into a number of fragments at least equal to a number of participant data storage nodes associated with the particular node set. Each participant data storage node sends a portion of the encrypted data to each of the other participant data storage nodes within the particular node set. Each participant data storage node processes received encrypted data and data remaining on the particular participant data storage node to obtain a processing result. Each participant data storage node sends the processing result to a proxy node, wherein the proxy node performs data statistical analysis based on the processing result.