Microsoft technology licensing, llc (20240320360). PRIVACY FILTERS AND ODOMETERS FOR DEEP LEARNING simplified abstract
Contents
PRIVACY FILTERS AND ODOMETERS FOR DEEP LEARNING
Organization Name
microsoft technology licensing, llc
Inventor(s)
[[:Category:Mathias François Roger L�cuyer of New York NY (US)|Mathias François Roger L�cuyer of New York NY (US)]][[Category:Mathias François Roger L�cuyer of New York NY (US)]]
PRIVACY FILTERS AND ODOMETERS FOR DEEP LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240320360 titled 'PRIVACY FILTERS AND ODOMETERS FOR DEEP LEARNING
The abstract discusses devices, systems, and methods for improving phishing webpage content detection by using a nested privacy filter architecture and a privacy loss budget.
- Instantiating an odometer with a nested privacy filter architecture
- Training a DL model with privacy filters of different sizes
- Maintaining a running total of privacy loss budget consumed during training
- Returning the size of the smallest privacy filter bigger than the total privacy loss budget consumed
Potential Applications: - Enhancing cybersecurity measures - Improving detection of phishing websites - Strengthening privacy protection online
Problems Solved: - Enhancing the accuracy of detecting malicious content - Mitigating privacy risks associated with online activities
Benefits: - Increased security against phishing attacks - Better protection of sensitive information - Enhanced user privacy online
Commercial Applications: Title: Advanced Phishing Detection System for Cybersecurity Companies This technology can be utilized by cybersecurity firms to offer enhanced protection against phishing attacks, attracting more clients concerned about online security.
Prior Art: Researchers can explore existing technologies related to privacy filters and phishing detection systems to understand the evolution of this innovation.
Frequently Updated Research: Stay updated on advancements in privacy filter architectures and phishing detection methods to ensure the technology remains at the forefront of cybersecurity measures.
Questions about Phishing Detection Technology: 1. How does the nested privacy filter architecture improve phishing webpage content detection? 2. What are the potential implications of this technology for online privacy protection?
Original Abstract Submitted
generally discussed herein are devices, systems, and methods for improving phishing webpage content detection. a method can include instantiating an odometer with a nested privacy filter architecture, the nested privacy filter including privacy filters of different, increasing sizes, training a dl model, maintaining, during training and by a privacy odometer that operates using the nested privacy filter, a running total of privacy loss budget consumed by the training, and responsive to a query for the total privacy loss budget consumed, returning, by the odometer, a size of a smallest privacy filter of the nested privacy filters that is bigger than the running total of the privacy loss budget.
