Distributed Data Query Aggregation for Secure Multi-Party Analysis
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Solution Overview
Problem
As data volume increases, secure multi-party computation (SMPC) technologies face significant challenges in computational and communication complexity, making it difficult to perform collaborative data analysis while ensuring data security among different data owners.
Innovation Solution
A method and apparatus for data query that involves obtaining and generating data fragments from multiple devices, using one-hot encoding and secret fragmentation to classify and aggregate data attributes securely, reducing complexity through efficient query protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If secure multi-party computation (SMPC) technology is used to ensure data security in collaborative analysis, then data security is improved, but computational complexity and communication complexity are significantly increased
Solution Approach 1:
The patent divides data into multiple fragments and distributes them across different devices. Each device processes only its local fragment, performing computations independently. The final result is obtained by aggregating the processed fragments, thus reducing the computational burden on any single device while maintaining data security through secret sharing.
Solution Approach 2:
The patent introduces an intermediary aggregation mechanism that combines results from multiple devices without requiring them to exchange raw data or perform complex multi-party computations. This intermediary layer simplifies the communication protocol and reduces the overall computational complexity while preserving the security guarantees of SMPC.
2Reliability
If secure multi-party computation (SMPC) technology is used to ensure data security in collaborative analysis, then data security is improved, but communication complexity is significantly increased
Solution Approach 1:
By segmenting data into fragments distributed across devices, the patent eliminates the need for extensive inter-device communication required by traditional SMPC. Each device communicates only with the central aggregation mechanism, significantly reducing the communication complexity from O(n²) to O(n) where n is the number of devices.
Solution Approach 2:
The patent extracts the aggregation function as a separate, simplified component that handles result combination. This extraction allows the complex multi-party computation to be replaced with simpler local processing and centralized aggregation, reducing the communication overhead between devices.
3Quantity of substance
If data volume increases to provide more comprehensive analysis, then data completeness is improved, but computational complexity and communication complexity are significantly increased
Solution Approach 1:
The patent segments large volumes of data into manageable fragments that can be processed in parallel across multiple devices. Each device handles a subset of the data independently, allowing the system to scale to larger data volumes without proportionally increasing the computational complexity on any single device.
Solution Approach 2:
The patent merges the processing results from multiple devices through a simplified aggregation mechanism. This merging approach allows the system to leverage large data volumes across distributed devices while maintaining computational efficiency through parallel processing and reduced communication requirements.
Data Source
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AI summary
Embodiment of the present disclosure provides method, apparatus, device and storage medium for data query. In the method, first data is obtained by a first device based on a request for a data query, where the request indicates a first data attribute to be classified and aggregated and at least a second data attribute and a third data attribute for classification, and the first data at least includes data associated with a second data attribute; and a third data fragment of a result of the data query is generated based on the first data fragment of the first data and the second data fragment of the second data received from the second device, where the second data at least includes data associated with the third data attribute, and the third data fragment includes an element corresponding to the first data attribute that is classified and aggregated based on at least the second data attribute and the third data attribute.