View source for International business machines corporation (20240313966). TRAINING NEURAL NETWORKS WITH NON-POLYNOMIAL ELEMENTS FOR HOMOMORPHIC ENCRYPTION COMPUTATIONS USING SUB-NETWORKS AND MULTI-LOSS simplified abstract

=TRAINING NEURAL NETWORKS WITH NON-POLYNOMIAL ELEMENTS FOR HOMOMORPHIC ENCRYPTION COMPUTATIONS USING SUB-NETWORKS AND MULTI-LOSS=


==Organization Name==

[[:Category:international business machines corporation|international business machines corporation]]


[[Category:international business machines corporation]]

==Inventor(s)==

[[:Category:Itamar Zimerman of Tel-Aviv (IL)|Itamar Zimerman of Tel-Aviv (IL)]][[Category:Itamar Zimerman of Tel-Aviv (IL)]]

[[:Category:Nir Drucker of Zichron Yaakov (IL)|Nir Drucker of Zichron Yaakov (IL)]][[Category:Nir Drucker of Zichron Yaakov (IL)]]

==TRAINING NEURAL NETWORKS WITH NON-POLYNOMIAL ELEMENTS FOR HOMOMORPHIC ENCRYPTION COMPUTATIONS USING SUB-NETWORKS AND MULTI-LOSS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240313966 titled 'TRAINING NEURAL NETWORKS WITH NON-POLYNOMIAL ELEMENTS FOR HOMOMORPHIC ENCRYPTION COMPUTATIONS USING SUB-NETWORKS AND MULTI-LOSS

The abstract of this patent application describes a system with a processor that can handle non-homomorphic encryption (HE) friendly analytics models containing non-polynomial elements. The processor is designed to train a substitution model by replacing the non-polynomial element with a sub-network featuring a polynomial replacement element.

*Simplified Explanation:
This system involves a processor that can work with encryption-friendly analytics models by replacing non-polynomial elements with polynomial elements.

*Key Features and Innovation:
- Processor capable of handling non-homomorphic encryption-friendly analytics models.
- Training a substitution model to replace non-polynomial elements with polynomial elements.

*Potential Applications:
- Secure data analytics
- Encrypted machine learning models
- Privacy-preserving data processing

*Problems Solved:
- Enhancing the security of analytics models
- Enabling secure data processing with encryption

*Benefits:
- Improved data privacy
- Enhanced security for sensitive information
- Efficient data processing with encryption

*Commercial Applications:
"Secure Data Analytics: Enhancing Privacy and Security in Modern Data Processing"

*Prior Art:
Readers can explore prior research on non-homomorphic encryption and secure data processing techniques.

*Frequently Updated Research:
Stay updated on advancements in encryption-friendly analytics models and secure data processing techniques.

Questions about the technology:
1. How does the processor handle non-polynomial elements in encryption-friendly analytics models?
2. What are the potential implications of using polynomial replacement elements in secure data processing?


==Original Abstract Submitted==

an example system includes a processor to receive a non-homomorphic encryption (he)-friendly analytics model including a non-polynomial element. the processor is to train a substitution model in which the non-polynomial element of the non-homomorphic encryption (he)-friendly analytics model is replaced with a sub-network including a polynomial replacement element.



[[Category:H04L9/30]]
[[Category:G06N3/08]]
[[Category:CPC_H04L9/3093]]