17967220. Authenticating Usage Data For Processing By Machine Learning Models simplified abstract (Dell Products L.P.)
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 17967220 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.
- Receiving a message from a software component that collects usage data associated with a user device and is signed with a private key.
- Authenticating the usage data based on a public key corresponding to a digital certificate.
- Processing at least a portion of the authenticated usage data by the machine learning application.
Potential Applications
This technology could be applied in various fields such as cybersecurity, data analytics, and user behavior analysis.
Problems Solved
This technology helps in ensuring the authenticity and integrity of usage data collected from user devices, which is crucial for accurate processing by machine learning models.
Benefits
The technology provides a secure way to authenticate usage data, leading to more reliable and trustworthy results from machine learning models.
Potential Commercial Applications
One potential commercial application of this technology could be in the development of secure data analytics platforms for businesses.
Possible Prior Art
Prior art in the field of data authentication and processing by machine learning models may include similar methods used in cybersecurity and data encryption technologies.
Unanswered Questions
How does this technology impact user privacy and data security?
This article does not delve into the specific implications of this technology on user privacy and data security.
What are the potential limitations or challenges in implementing this technology on a large scale?
The article does not address the potential obstacles or limitations that may arise when implementing this technology on a larger scale.
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.
