International business machines corporation (20240291655). TRAINING ARIMA TIME-SERIES MODELS UNDER FULLY HOMOMORPHIC ENCRYPTION USING APPROXIMATING POLYNOMIALS simplified abstract
Contents
- 1 TRAINING ARIMA TIME-SERIES MODELS UNDER FULLY HOMOMORPHIC ENCRYPTION USING APPROXIMATING POLYNOMIALS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TRAINING ARIMA TIME-SERIES MODELS UNDER FULLY HOMOMORPHIC ENCRYPTION USING APPROXIMATING POLYNOMIALS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about the Technology
- 1.13 Original Abstract Submitted
TRAINING ARIMA TIME-SERIES MODELS UNDER FULLY HOMOMORPHIC ENCRYPTION USING APPROXIMATING POLYNOMIALS
Organization Name
international business machines corporation
Inventor(s)
Allon Adir of Kiryat Tivon (IL)
Ramy Masalha of Kafr Qari (IL)
Eyal Kushnir of Kfar Vradim (IL)
Ehud Aharoni of Kfar Saba (IL)
TRAINING ARIMA TIME-SERIES MODELS UNDER FULLY HOMOMORPHIC ENCRYPTION USING APPROXIMATING POLYNOMIALS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240291655 titled 'TRAINING ARIMA TIME-SERIES MODELS UNDER FULLY HOMOMORPHIC ENCRYPTION USING APPROXIMATING POLYNOMIALS
Simplified Explanation
The patent application describes a system where a processor receives encrypted time series data, trains a model on the encrypted data, and sends an encrypted report back to the client device.
Key Features and Innovation
- Processor receives ciphertext with fully homomorphic encrypted time series data
- Processor trains an ARIMA model on the encrypted data using estimated error and approximating polynomials
- Processor generates an encrypted report and sends it back to the client device
Potential Applications
This technology could be used in secure data analysis, financial forecasting, and remote data processing applications.
Problems Solved
This technology addresses the need for secure data analysis and processing while maintaining data privacy and confidentiality.
Benefits
- Enhanced data security and privacy
- Ability to perform data analysis on encrypted data
- Remote data processing capabilities
Commercial Applications
- Secure data analysis services for financial institutions
- Remote data processing solutions for healthcare providers
- Confidential data forecasting services for businesses
Prior Art
Readers can explore prior research on fully homomorphic encryption, ARIMA models, and encrypted data analysis to understand the background of this technology.
Frequently Updated Research
Researchers are continually exploring advancements in fully homomorphic encryption and encrypted data analysis techniques to improve the efficiency and security of data processing.
Questions about the Technology
How does fully homomorphic encryption impact data analysis?
Fully homomorphic encryption allows for data analysis to be performed on encrypted data without compromising data privacy.
What are the potential limitations of using encrypted data for forecasting models?
Using encrypted data for forecasting models may introduce computational overhead and complexity in the data analysis process.
Original Abstract Submitted
an example system can include a processor to receive a ciphertext including a fully homomorphic encrypted (fhe) time series from a client device. the processor can train an arima model on the ciphertext using an estimated error and approximating polynomials. the processor can generate an encrypted report and send the encrypted report to the client device.
