Oblivious Data Transfer for Privacy-Preserving Statistical Calculations
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Solution Overview
Problem
In business data processing, data leakage often occurs when multiple parties collaborate for statistical calculations, as existing methods fail to protect the privacy of individual data sets.
Innovation Solution
A data processing method and apparatus that splits feature data into sub-data using a splitting parameter, performs oblivious transfer, and calculates summation results to enable collaborative statistical calculations without revealing proprietary data, utilizing a splitting parameter set and oblivious transfer protocols between devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple parties share their data for collaborative statistical calculations, then the completeness and accuracy of data indicators improve, but data privacy and security deteriorate due to potential data leakage
Solution Approach 1:
The patent segments feature data into multiple sub-data pieces using a splitting parameter. Each party holds only a portion of the data (sub-data), and no single party possesses the complete original data. This segmentation enables collaborative statistical calculations while preventing any individual party from accessing the full dataset, thus resolving the contradiction between data completeness and privacy protection.
2Productivity
If parties perform collaborative data processing, then the statistical calculation capability improves, but the complexity of the data processing system increases due to privacy protection requirements
Solution Approach 1:
The patent introduces an intermediary mechanism (oblivious transfer protocol and splitting parameter) that mediates the data exchange between parties. This intermediary enables collaborative statistical calculations without requiring direct sharing of raw data, thus maintaining system functionality while managing complexity through standardized privacy-preserving operations.
3Ease of operation
If parties use traditional data sharing methods for joint calculations, then the ease of operation improves, but data security deteriorates
Solution Approach 1:
The patent transforms the data representation by introducing splitting parameters that convert original feature data into multiple sub-data pieces. This parameter change enables parties to perform calculations on transformed data without exposing the original information, thus maintaining operational ease while significantly improving data security through mathematical transformation.
Data Source
AI summary
Implementations of this specification provide methods and apparatuses for oblivious data transfer between computing devices. An example method includes receiving, by a second computing device, an oblivious transfer from a first computing device. The first computing device splits feature data in a feature dataset into a plurality of sub-data and uses the plurality of sub-data as input, and the second computing device uses label data in a label dataset as input. The second computing device selects target sub-data from the plurality of sub-data input by the first computing device, and determines a first summation result of the selected target sub-data. The second computing device receives from the first computing device a second summation result of the one or more splitting parameters in the splitting parameter set, and calculates a statistical indicator based on the first summation result and the second summation result.