Secure Enclave Data Processing for Privacy-Preserving Recommendations
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Solution Overview
Problem
Existing technologies face challenges in ensuring privacy and security during data processing, particularly in environments with increased encryption levels, leading to computational burdens and reduced performance, while banks and merchants are reluctant to share consumer data due to privacy concerns and loss of control.
Innovation Solution
A data aggregator system utilizing secure enclaves with isolated memory regions and secure processing technologies to protect data, ensuring only authorized systems can access encrypted data, and implementing machine learning architectures within these enclaves to maintain privacy and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If encryption and decryption steps are added to protect consumer privacy during data processing, then privacy protection is improved, but computational burden increases and performance decreases
Solution Approach 1:
The system segments data processing into multiple isolated secure enclaves, each handling specific data sets from different sources. This segmentation allows parallel processing of encrypted data across multiple enclaves, distributing the computational burden while maintaining strong encryption protections for each segment.
Solution Approach 2:
The patent introduces secure enclaves as intermediary processing environments that act as trusted mediators between data sources and data consumers. These enclaves perform encryption, decryption, and data processing operations internally, shielding the computational complexity from external systems while ensuring privacy protection through hardware-based security boundaries.
2Reliability
If data is stored and processed in secure enclaves with isolated memory regions, then security against malicious attacks is improved, but memory space constraints increase
Solution Approach 1:
The system implements nested secure enclaves where multiple isolated memory regions are hierarchically organized. Data sets from different sources are stored in separate nested enclaves, each with its own isolated memory space. This nesting allows efficient memory utilization by allocating only the necessary memory to each enclave while maintaining strong isolation boundaries that prevent unauthorized access.
Solution Approach 2:
Each secure enclave is configured with specific memory allocation and security parameters tailored to its particular data set and processing requirements. This local quality approach allows optimization of memory usage for each enclave individually, ensuring that security constraints are met while minimizing overall memory consumption across the system.
3Adaptability or versatility
If multiple data sets from separate computing systems are aggregated and processed, then data utility for machine learning is improved, but risk of data breaches and loss of data control increases
Solution Approach 1:
The system segments aggregated data into distinct isolated enclaves, each containing data from a specific source. Machine learning models are trained by passing data through these segmented enclaves in a controlled manner, allowing the model to learn from multiple data sets while preventing any single enclave from exposing its complete data set. This segmentation maintains data utility for training while minimizing breach risk through isolation.
Solution Approach 2:
Secure enclaves serve as intermediary processing layers between multiple data sources and the machine learning model. These enclaves perform data preprocessing, transformation, and feature extraction internally, passing only processed results to the model. This intermediary approach enables effective use of multiple data sets for training while protecting the original data from direct exposure to the model or external systems.
Data Source
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AI summary
A system for processing data within a Trusted Execution Environment (TEE) of a processor is provided. The system may include: a trust manager unit for verifying identity of a partner and issuing a communication key to the partner upon said verification of identity; at least one interface for receiving encrypted data from the partner encrypted using the communication key; a secure database within the TEE for storing the encrypted data with a storage key and for preventing unauthorized access of the encrypted data within the TEE; and a recommendation engine for decrypting and analyzing the encrypted data to generate recommendations based on the decrypted data.