Dell products l.p. (20240126859). Authenticating Usage Data For Processing By Machine Learning Models simplified abstract
Contents
- 1 Authenticating Usage Data For Processing By Machine Learning Models
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Authenticating Usage Data For Processing By Machine Learning Models - 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
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
Authenticating Usage Data For Processing By Machine Learning Models
Organization Name
Inventor(s)
Tianxiang Chen of Shanghai (CN)
Authenticating Usage Data For Processing By Machine Learning Models - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240126859 titled 'Authenticating Usage Data For Processing By Machine Learning Models
Simplified Explanation
The abstract describes methods, apparatus, and processor-readable storage media for authenticating usage data for processing by machine learning models.
- The software component collects usage data associated with a user device, signs it using a private key corresponding to a digital certificate, and deploys it in the kernel space of the operating system.
- The machine learning application installed in the user space of the operating system receives a message from the software component containing the signed usage data.
- The machine learning application authenticates the usage data based on a public key corresponding to the digital certificate.
- In response to the authentication result, the machine learning application processes at least a portion of the authenticated usage data.
Potential Applications
This technology can be applied in various industries such as cybersecurity, data analytics, and artificial intelligence.
Problems Solved
This technology helps in ensuring the authenticity and integrity of usage data collected from user devices, which is crucial for accurate machine learning model training and decision-making.
Benefits
The technology provides a secure and reliable way to authenticate usage data, enhancing the trustworthiness of machine learning models and their outputs.
Potential Commercial Applications
- Secure data analytics platforms
- Fraud detection systems
- Personalized recommendation engines
Possible Prior Art
One possible prior art could be the use of digital certificates for data authentication in secure communication protocols.
What are the potential limitations of this technology?
- The effectiveness of the authentication process may depend on the security measures implemented in the digital certificate and key management.
- Compatibility issues with different operating systems and software components could arise.
How does this technology compare to existing authentication methods for usage data?
This technology offers a more robust and secure way to authenticate usage data compared to traditional methods like username/password authentication or basic encryption techniques. By leveraging digital certificates and public-private key pairs, it enhances data integrity and trustworthiness in machine learning applications.
Original Abstract Submitted
methods, apparatus, and processor-readable storage media for authenticating usage data for processing by machine learning models are provided herein. an example method includes receiving, by a machine learning application installed in a user space of an operating system of a user device, a message from a software component, wherein the software component is: configured to collect usage data associated with the user device; signed with using private key corresponding to a digital certificate by an application installed on the user device; and deployed in a kernel space of the operating system, and wherein the message comprises usage data signed using the private key; authenticating, by the machine learning application, the usage data based on a public key corresponding to the digital certificate; and processing, by the machine learning application in response to a result of the authenticating, at least a portion of the authenticated usage data.
