Trusted Execution Space Data Mining System
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Solution Overview
Problem
Existing data modeling techniques for predicting events, such as infectious disease epidemics, suffer from low prediction accuracy due to insufficient data support resulting from data privacy protection limitations.
Innovation Solution
A data mining system that transfers data from a first trusted execution space to a second trusted execution space through a transfer device, allowing the second trusted execution space to determine data mining results based on the transferred data and mining-related data, while ensuring data privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is shared across multiple institutions to improve prediction accuracy, then prediction accuracy is improved, but data privacy protection is compromised
Solution Approach 1:
The patent introduces a trusted execution environment (TEE) as an intermediary between data providers and data users. The TEE securely stores and processes sensitive data without exposing it to external parties. Data calling requests are mediated through the TEE, which validates permissions and returns only computation results, not the underlying data. This resolves the contradiction by enabling data sharing for improved prediction accuracy while maintaining data privacy through the TEE intermediary layer.
2Object-affected harmful factors
If data is restricted to protect privacy, then data privacy is protected, but prediction accuracy deteriorates due to insufficient data support
Solution Approach 1:
The patent segments the data access process into distinct components: data storage in TEE, permission verification, data calling requests, and result retrieval. By segmenting the system into trusted execution spaces with controlled access interfaces, the patent enables privacy protection through restricted access while still allowing computational use of the data. Multiple institutions can contribute data to the TEE, and data users can perform computations on aggregated data without accessing individual records, thus improving prediction accuracy while maintaining privacy.
3Object-affected harmful factors
If multiple institutions contribute data through trusted execution spaces, then data privacy is ensured, but system complexity increases
Solution Approach 1:
The patent implements a universal trusted execution environment that can serve multiple data providers and data users through a standardized interface. The TEE performs multiple functions: secure data storage, permission verification, data processing, and result generation. By creating a multi-functional TEE system that handles various data types and access patterns through a common architecture, the patent reduces overall system complexity compared to implementing separate secure systems for each institution. The standardized data calling request and response mechanisms provide universal access control across different entities.
Data Source
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AI summary
Embodiments of the present disclosure provide a data mining system, a data mining method, a data mining apparatus, an electronic device and a storage medium. The data mining system includes a transfer device (110), a first trusted execution space (120) and a second trusted execution space (130). The transfer device (110) is configured to receive a data calling request of the second trusted execution space (130), obtain data to be called from the first trusted execution space (120) according to the data calling request, and provide the data to be called to the second trusted execution space (130), so as to perform data mining based on the data to be called and the mining-related data to obtain a data mining result and to provide the data mining result to a device of the data user.