How to Use Homomorphic Encryption in the Real World

Homomorphic encryption is a privacy-enhancing technology that allows computations on encrypted data without decryption, ensuring data privacy across various applications. It enables the execution of machine learning computations on encrypted data, where data owners like Alice can gain insights without exposing their data or the model used, as demonstrated in a collaboration with Bob's cloud service. Similarly, it facilitates encrypted queries on public databases, allowing users to privately retrieve information such as stock prices without revealing their specific interests, exemplified by Alice's interaction with Bob's stock database. In another use case, homomorphic encryption combined with secret sharing enables multiple parties, such as hospitals, to collaboratively train machine learning models on their combined encrypted patient data without compromising individual privacy, as evidenced by the example involving Alice, Bob, and Charles. The technology ensures that the computations appear as gibberish to anyone other than the data owner, maintaining confidentiality and security, and can be further enhanced by integrating differential privacy to prevent the leakage of personally identifiable information.

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