| Title | Application of Homomorphic Encryption in Machine Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Behera, S., Prathuri, J. R. |
| Conference Name | 2020 2nd PhD Colloquium on Ethically Driven Innovation and Technology for Society (PhD EDITS) |
| Keywords | Ciphers, cryptography, Encryption, homomorphic encryption, human factors, linear regression, machine learning, machine learning algorithms, Metrics, Prediction algorithms, pubcrawl, Resiliency, Scalability |
| Abstract | The linear regression is a machine learning algorithm used for prediction. But if the input data is in plaintext form then there is a high probability that the sensitive information will get leaked. To overcome this, here we are proposing a method where the input data is encrypted using Homomorphic encryption. The machine learning algorithm can be used on this encrypted data for prediction while maintaining the privacy and secrecy of the sensitive data. The output from this model will be an encrypted result. This encrypted result will be decrypted using a Homomorphic decryption technique to get the plain text. To determine the accuracy of our result, we will compare it with the result obtained after applying the linear regression algorithm on the plain text. |
| DOI | 10.1109/PhDEDITS51180.2020.9315305 |
| Citation Key | behera_application_2020 |
Application of Homomorphic Encryption in Machine Learning