Abstract
AbstractThe modern world is moving towards an intelligent future where AI and ML will be playing an important role in improving technologies across all domains. The most basic requirement for training an ML model is data. Without the availability of data, there is no use for AI or ML. Data is freely and openly available but still, the privacy of the data is a deeper concern, which can create hurdles in developing new technologies. This paper suggests various ways which can be used to train ML models on such private data while maintaining the privacy of both the data as well as the ML model, using homomorphic encryption. This paper implemented a neural network on homomorphic encryption and proved the increase in accuracy of finding attacks over data on the fly and data at still. Thus, this paper mainly focuses on what happens when we apply Neural networks along with Homomorphic Encryption over data on still and data on the fly.KeywordsHomomorphic EncryptionNeural NetworksSecurityAIAttack-detection
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