Secure Signal Processing and Secure Machine Learning using Fully Homomorphic Encryption

2021 ◽  
Author(s):  
Thomas M. Shortell
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Homomorphic Encryption ◽  
Fully Homomorphic Encryption

A Survey on Privacy-Preserving Machine Learning with Fully Homomorphic Encryption

2021 ◽  
pp. 115-129
Author(s):  
Luis Bernardo Pulido-Gaytan ◽  
Andrei Tchernykh ◽  
Jorge M. Cortés-Mendoza ◽  
Mikhail Babenko ◽  
Gleb Radchenko
Keyword(s):  
Machine Learning ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Fully Homomorphic Encryption

scholarly journals Sensitivity and Positive Prediction of Secured Electrocardiograph (ECG) Transmission using Fully Homomorphic Encryption Technique (FHE)

2021 ◽  
pp. 97-104
Author(s):  
Dr. P. Balashanmuga Vadivu ◽  
K. Narmatha
Keyword(s):  
Signal Processing ◽  
Homomorphic Encryption ◽  
Heart Diseases ◽  
Digital Signal ◽  
Ecg Signal ◽  
Medical Provider ◽  
Fully Homomorphic Encryption ◽  
Positive Prediction ◽  
Clinical Method ◽  
Ecg Signals

Health connected is a technology that links medical devices, telecommunications and security techniques. It empowers patients to be observed and treated remotely from their homes. Patient’s healthcare records with a connected healthcare system should be stored securely before transmitted for further investigation and interpretation. Electrocardiogram (ECG) is the clinical method utilized to screen heart execution and utilized for the detection of various arrhythmias. For diagnostic purposes, individuals with a background of heart diseases have long records of ECGs, which results in the requirement of a large amount of storage space and labor. Hence, there is a requirement for a system that involves digital signal processing and signal security so that the spared information is made sure about at one spot and an only authentic individual can see and utilize this ECG signal for additional findings. This study presents a set of security solutions that can be deployed in a connected healthcare territory, which includes the fully homomorphic encryption (FHE) techniques used to secure the ECG signals. The study helps the medical provider to record ECG signals confidentially and to prevent mistreatment. The study focuses on Pan and Tompkins algorithm methods for the detection of the ECG Signal. As a result, the output of the Pan and Tompkins algorithm for ECG signal processing with the FHE technique shows a sensitivity of 92.59% and a positive prediction of 90.00%.

scholarly journals Bibliometrics of Machine Learning Research Using Homomorphic Encryption

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2792
Author(s):  
Zhigang Chen ◽  
Gang Hu ◽  
Mengce Zheng ◽  
Xinxia Song ◽  
Liqun Chen
Keyword(s):  
Machine Learning ◽  
Homomorphic Encryption ◽  
Relative Increase ◽  
Research Literature ◽  
Fully Homomorphic Encryption ◽  
Web Based ◽  
Research Areas ◽  
The Us ◽  
Automated Tools ◽  
Citation Statistics

Since the first fully homomorphic encryption scheme was published in 2009, many papers have been published on fully homomorphic encryption and its applications. Machine learning is one of the most interesting applications and has drawn a lot of attention from researchers. To better represent and understand the field of Homomorphic Encryption in Machine Learning (HEML), this paper utilizes automated citation and topic analysis to characterize the HEML research literature over the years and provide the bibliometrics assessments for this burgeoning field. This is conducted by using a bibliometric statistical analysis approach. We make use of web-based literature databases and automated tools to present the development of HEML. This allows us to target several popular topics for in-depth discussion. To achieve these goals, we have chosen the well-established Scopus literature database and analyzed them through keyword counts and Scopus relevance searches. The results show a relative increase in the number of papers published each year that involve both homomorphic cryptography and machine learning. Using text mining of articles titles, we have found that cloud computing is a popular topic in this field, which also includes neural networks, big data, and the Internet of Things. The analysis results show that China, the US, and India have generated almost half of all the research contributions in HEML. The citation statistics, keyword statistics, and topic analyses give us a quick overview of the development of the field, which can be of great help to new researchers. It is also possible to apply our methodology to other research areas, and we see great value in this approach.

scholarly journals A Comparative Study of Homomorphic Encryption Schemes Using Microsoft SEAL

2021 ◽  
Vol 2128 (1) ◽  
pp. 012021
Author(s):  
Shereen Mohamed Fawaz ◽  
Nahla Belal ◽  
Adel ElRefaey ◽  
Mohamed Waleed Fakhr
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Comparative Study ◽  
Data Aggregation ◽  
Homomorphic Encryption ◽  
Arithmetic Operations ◽  
Fully Homomorphic Encryption ◽  
Encrypted Data ◽  
Encryption Schemes ◽  
The Difference

Abstract Fully homomorphic encryption (FHE) technology is a method of encrypting data that allows arbitrary calculations to be computed. Machine learning (ML) and many other applications are relevant to FHE such as Cloud Computing, Secure Multi-Party, and Data Aggregation. Only the authenticated user has the authority to decrypt the ciphertext and understand its meaning, as encrypted data can be computed and processed to produce an encrypted output. Homomorphic encryption uses arithmetic circuits that focus on addition and multiplication, allowing the user to add and multiply integers while encrypted. This paper discusses the performance of the Brakerski-Fan-Vercauteren scheme (BFV) and Cheon, Kim, Kim, and Song (CKKS) scheme using one of the most important libraries of FHE “Microsoft SEAL”, by applying certain arithmetic operations and observing the time consumed for every function applied in each scheme and the noise budget after every operation. The results obtained show the difference between the two schemes when applying the same operation and the number of sequential operations each can handle.

A Neural Network Application of Fully Homomorphic Encryption for Cloud Computing

2020 ◽  
Author(s):  
Megha Kolhekar ◽  
Ashish Pandey ◽  
Ayushi Raina ◽  
Rijin Thomas ◽  
Vaibhav Tiwari ◽  
...  
Keyword(s):  
Neural Network ◽  
Cloud Computing ◽  
Homomorphic Encryption ◽  
Fully Homomorphic Encryption ◽  
Network Application
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