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@ -48,3 +48,17 @@ print(f"Decrypted multiplication result: {result_mul}, Expected: {num1*num2}")
This script creates an instance of `Pyfhel`, generates a public and secret key with `keyGen()`, encrypts two integers using `encryptInt()`, adds and multiplies them, then decrypts the results using `decryptInt()`. The decrypted results should be equal to the results of adding and multiplying the original, unencrypted numbers.
Remember that this is a simplified example. In a real-world scenario, key management and ensuring the security of the encryption and decryption operations are crucial and more complex. Furthermore, full homomorphic encryption is a computationally intensive task and may not be suitable for all types of data or applications.
## References
A few resources that can provide a deeper understanding of homomorphic encryption:
1. [Homomorphic Encryption Standard](https://homomorphicencryption.org/): The official site for the Homomorphic Encryption Standard, containing detailed technical resources and documentation.
2. [Homomorphic Encryption Notations, Schemes, and Circuits](https://eprint.iacr.org/2014/062.pdf): A technical paper providing a more mathematical and in-depth exploration of various homomorphic encryption schemes.
3. [Cryptonets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/04/CryptonetsTechReport.pdf): A research paper from Microsoft Research demonstrating the application of homomorphic encryption in machine learning.
4. [Pyfhel Github Repository](https://github.com/ibarrond/Pyfhel): The Github repository for Pyfhel, a Python library for Homomorphic Encryption, which includes code examples and documentation.
Homomorphic encryption is a complex field that requires a decent understanding of cryptography. It's recommended to have a good grasp of the basics of cryptography before diving into homomorphic encryption.