Computation Techniques for Encrypted Data using Python Remote

Imagine you have important documents such as credit card statements of expenditure or other financial documents and you want to protect them from illegal entities. You will store them in safe and lock it with a key. This process is known as encryption. Now if you want analyze your spending habits or process financial accounts related your business then you need to take out the documents out of safe. This process is known as decryption. This step has a problem i.e.; the moment you take out the documents from the safe they become vulnerable to illegal entities. To avoid such situations, we need a mechanism to process documents without taking them out of the safe. It is possible through homomorphic encryption i.e., by performing computations on encrypted data and this talks addresses techniques to perform computations of encrypted data. **Implementation** The cryptographic algorithms have been implemented in Python. Also, the Python code has been written to verify the homomorphic properties of cryptographic algorithms. Homomorphic encryption techniques are computation intensive. To reduce the number of computations we used the Fast Fourier Transform (FFT) and Karatsuba methods for performing arbitrary computations such as multiplication over large integers. Next to demonstrate the implementation of a secure machine learning model we combined cryptography package in python and PyBRAIN Artificial Intelligence package.