Privacy Preservation of Business Forecasting Using Homomorphic Encryption

Abstract

Data privacy is very much essential in this digital world. Data privacy prevents the information of an organization from fraudulent activities such as hacking, phishing, and identity theft. Machine learning is an emerging technology. But a huge amount of data is required for training the Machine learning model. When an organization wants to analyze their profit rate it has to send its data to third party which may reveal organization's business tactics or sensitive data. Hence, there is always a risk of data privacy. So, privacy preserving is used. Privacy preserving prevents data leakage from machine learning algorithms. There are many privacy preserving machine learning strategies which are used for data privacy. Homomorphic Encryption is one such technique. In homomorphic encryption, the data to be fed to train the machine learning model is encrypted. The encrypted data is then fed to the machine learning model. The machine learning model performs the required computation and returns the result in encrypted form, which on decryption returns the required output