Secure Neural Network Processing with Just-in-Time Decryption
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Solution Overview
Problem
In a multi-tenant or cloud computing environment, artificial neural networks face security challenges as weights data, input data, and neural network models are vulnerable to unauthorized access due to their storage and transmission in unencrypted forms, which can lead to interception and misuse.
Innovation Solution
Implementing a secure neural network processing environment with encryption of weights data, input data, and neural network models, where decryption occurs just-in-time during computations, and using a centralized security manager to control key distribution and access, ensuring that only authorized entities can access the decrypted data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If weights data, input data, and neural network models are stored and transmitted in unencrypted forms, then data accessibility and processing speed are improved, but security and vulnerability to unauthorized access deteriorate
Solution Approach 1:
Data is encrypted before storage in memory devices, establishing security protection in advance before any access or processing occurs. This preliminary encryption action ensures that weights data, input data, and neural network models are protected from unauthorized access while maintaining system functionality.
Solution Approach 2:
A decryption engine acts as an intermediary component between encrypted data storage and the computing engine. This mediator decrypts data only when needed for processing, providing controlled access that maintains security while enabling computation. The decryption engine serves as a buffer that protects sensitive data while allowing authorized processing.
2Reliability
If data is encrypted during storage and transmission, then security against unauthorized access is improved, but processing time and computational overhead increase
Solution Approach 1:
The decryption operation is extracted as a separate, dedicated engine component that operates independently from the main computing engine. This extraction allows decryption to be performed in a specialized unit optimized for security operations, while the computing engine focuses on neural network computations, minimizing overall processing impact.
Solution Approach 2:
The system rapidly decrypts data in a just-in-time manner immediately before processing, minimizing the time encrypted data spends in the system. This rushing through the decryption process ensures security is maintained while the brief decryption window minimizes processing overhead and keeps the system productive.
3Reliability
If centralized key distribution is implemented, then control over data access is improved, but system complexity and key management overhead increase
Solution Approach 1:
The security manager serves multiple functions within a single centralized component: it generates encryption keys, distributes them to appropriate entities, manages key lifecycle, and controls access permissions. This multi-functionality consolidates key management complexity into a dedicated universal component rather than scattering it across multiple systems.
Solution Approach 2:
The system implements automated key distribution and management processes where the security manager autonomously handles key generation, distribution, and revocation without requiring manual intervention for each operation. This self-service capability reduces operational overhead while maintaining centralized control over data access.
Data Source
AI summary
Systems and methods for performing neural network processing are provided. In one example, a system comprises a neural network processor comprising: a data decryption engine that receives encrypted data and decrypts the encrypted data, the encrypted data comprising at least one of: encrypted weights data, encrypted input data, or encrypted instruction data related to a neural network model; and a computing engine that receives the weights data and perform computations of neural network processing using the input data and the weights data and based on the instruction data.