Statistical Estimation Block Design for Privacy-Preserving Data Processing
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Solution Overview
Problem
The increasing collection and use of personal data in the big data era pose significant challenges for privacy protection, as existing methods are insufficient to prevent the inference of original data from processed data, leading to potential privacy infringement.
Innovation Solution
A data processing estimating method using a statistical estimation block design is introduced, which generates modification data randomly along a conditional distribution for the original data, and estimates the distribution of the original data using an estimation function based on the block design, thereby enhancing privacy protection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If original data is processed and provided to data users, then data utility and communication efficiency are improved, but privacy protection deteriorates as data providers can infer original data
Solution Approach 1:
The patent creates a modified copy of the original data by generating modification data according to a conditional distribution. This copy preserves statistical properties for analysis while altering individual data points to prevent inference of the original data, thus resolving the contradiction between data utility and privacy protection
Solution Approach 2:
The patent transforms the original data into modified data by changing data parameters according to a conditional distribution P(Y|X). This parameter transformation maintains the statistical structure needed for analysis while altering individual values to ensure privacy protection
2Loss of information
If modification data is generated randomly along conditional distribution, then privacy protection is improved, but statistical accuracy deteriorates
Solution Approach 1:
The patent applies parameter changes by transforming original data X into modified data Y according to a conditional distribution P(Y|X). This transformation is designed to preserve statistical properties (mean, variance, distribution shape) while altering individual data points to ensure privacy protection
Solution Approach 2:
The patent uses feedback mechanisms where the conditional distribution P(Y|X) is designed based on the relationship between original and modified data. The estimation function f(Y) is calibrated using this distribution to ensure that statistical estimates remain accurate despite the random modification, creating a feedback loop that maintains both privacy and accuracy
3Productivity
If statistical estimation block design is implemented, then communication efficiency is improved, but device complexity increases
Solution Approach 1:
The patent segments the data processing system into distinct functional components: data providers who generate modification data, communication channels for data transmission, and data users who perform estimation. This segmentation allows each component to be optimized independently, improving communication efficiency while managing system complexity through modular architecture
Solution Approach 2:
The patent creates a universal framework where the conditional distribution P(Y|X) and estimation function f(Y) can be applied to various types of data (categorical, numerical, temporal) without requiring separate processing mechanisms. This multi-functionality improves communication efficiency across different data types while reducing overall system complexity through a unified approach
Data Source
AI summary
A data processing estimating method for privacy protection using a statistical estimation block design and a system for performing the method is disclosed. A data processing estimating system for privacy protection includes a block design unit for designing block designs for statistical estimation shared between data providers and data users; a modification data generating unit for generating modification data in a random manner along a conditional distribution for the original data of the above statistical estimation block design; and a data distribution estimating unit for estimating the distribution of the original data using an estimation function based on the statistical estimation block design. Accordingly, data processing techniques and estimation functions are provided by utilizing statistical estimation block designs shared between data providers and data users, thereby preventing leakages of sensitive personal information such as personal photos, purchase records, and locations included in the collected data, it is possible to increase statistical accuracy and communication efficiency while satisfying the goal of privacy protection by preventing leakage of sensitive personal information.