Publication | Closed Access
Application of Homomorphic Encryption in Machine Learning
21
Citations
4
References
2020
Year
Unknown Venue
Privacy ProtectionEngineeringMachine LearningInformation SecurityCryptographic TechnologyInformation ForensicsPlain TextData ScienceLinear Regression AlgorithmData PrivacyCryptosystemComputer SciencePrivacy LeakageData SecurityCryptographyEncryptionCloud CryptographyLinear RegressionHomomorphic Encryption
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.
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