17966313. METHOD, DEVICE, AND COMPUTER PROGRAM PRODUCT FOR EXECUTING COMPUTER PROGRAMS simplified abstract (Dell Products L.P.)
Contents
- 1 METHOD, DEVICE, AND COMPUTER PROGRAM PRODUCT FOR EXECUTING COMPUTER PROGRAMS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD, DEVICE, AND COMPUTER PROGRAM PRODUCT FOR EXECUTING COMPUTER PROGRAMS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications of this Technology
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
METHOD, DEVICE, AND COMPUTER PROGRAM PRODUCT FOR EXECUTING COMPUTER PROGRAMS
Organization Name
Inventor(s)
Tianxiang Chen of Shanghai (CN)
METHOD, DEVICE, AND COMPUTER PROGRAM PRODUCT FOR EXECUTING COMPUTER PROGRAMS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17966313 titled 'METHOD, DEVICE, AND COMPUTER PROGRAM PRODUCT FOR EXECUTING COMPUTER PROGRAMS
Simplified Explanation
Embodiments of the present disclosure relate to a method, a device, and a computer program product for executing computer programs. The method includes implementing multiple executions of a deep neural network that includes a hooking portion outside a trusted execution environment (TEE), where an operator in the hooking portion, when executed outside the TEE, invokes a corresponding execution operator in the TEE. During this process, the method involves determining a computation graph corresponding to the hooking portion based on the invocation of the corresponding execution operator in the TEE by the operator in the hooking portion. The method further includes executing the execution operator corresponding to the hooking portion in the TEE based on the computation graph during the execution of the deep neural network after the multiple executions.
- Implementing multiple executions of a deep neural network with a hooking portion outside a trusted execution environment (TEE).
- Determining a computation graph corresponding to the hooking portion based on the invocation of the corresponding execution operator in the TEE.
- Executing the execution operator corresponding to the hooking portion in the TEE during the execution of the deep neural network after the multiple executions.
Potential Applications
This technology could be applied in secure computing environments, such as in the healthcare industry for processing sensitive patient data, or in financial institutions for secure transaction processing.
Problems Solved
This technology helps in ensuring the security and integrity of computations by executing critical operations within a trusted execution environment, protecting against potential threats or attacks on the system.
Benefits
- Enhanced security for executing computer programs - Improved protection of sensitive data - Efficient and reliable execution of deep neural networks
Potential Commercial Applications of this Technology
One potential commercial application of this technology could be in the development of secure cloud computing services for businesses that require high levels of data protection and privacy.
Possible Prior Art
Prior art in the field of secure computing environments includes techniques for secure data processing, encryption methods, and secure hardware modules for protecting sensitive information.
Unanswered Questions
How does this technology compare to existing methods for secure computation in terms of performance and efficiency?
This article does not provide a direct comparison with existing methods for secure computation in terms of performance and efficiency. Further research or testing may be needed to evaluate the effectiveness of this technology in comparison to other methods.
What are the potential limitations or drawbacks of implementing this technology in practical applications?
The article does not address potential limitations or drawbacks of implementing this technology in practical applications. It would be important to consider factors such as cost, compatibility with existing systems, and potential vulnerabilities that could arise in real-world scenarios.
Original Abstract Submitted
Embodiments of the present disclosure relate to a method, a device, and a computer program product for executing computer programs. The method includes implementing multiple executions of a deep neural network that includes a hooking portion outside a trusted execution environment (TEE), wherein an operator in the hooking portion, when executed outside the TEE, invokes a corresponding execution operator in the TEE. During the period, the method includes determining, on the basis of the invocation of the corresponding execution operator in the TEE by the operator in the hooking portion, a computation graph corresponding to the hooking portion. The method further includes executing, on the basis of the computation graph, the execution operator corresponding to the hooking portion in the TEE during the execution of the deep neural network after the multiple executions.
