View source for Dell products l.p. (20240126859). Authenticating Usage Data For Processing By Machine Learning Models simplified abstract

=Authenticating Usage Data For Processing By Machine Learning Models=


==Organization Name==

[[:Category:dell products l.p.|dell products l.p.]]


[[Category:dell products l.p.]]

==Inventor(s)==

[[:Category:Jinpeng Liu of Shanghai (CN)|Jinpeng Liu of Shanghai (CN)]][[Category:Jinpeng Liu of Shanghai (CN)]]

[[:Category:Tianxiang Chen of Shanghai (CN)|Tianxiang Chen of Shanghai (CN)]][[Category:Tianxiang Chen of Shanghai (CN)]]

[[:Category:Sarah Evans of Parker CO (US)|Sarah Evans of Parker CO (US)]][[Category:Sarah Evans of Parker CO (US)]]

[[:Category:Zhen Jia of Shanghai (CN)|Zhen Jia of Shanghai (CN)]][[Category:Zhen Jia 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.



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