FHE stands for Fully Homomorphic Encryption, which is a type of encryption scheme that enables computation on ciphertexts directly, without the need for decryption. This means that the encrypted data remains encrypted throughout the entire computation process, and the result of the computation is also encrypted, without any party having access to the plaintext data at any point.
This property of FHE is particularly useful in scenarios where privacy is a concern, such as in cloud computing, where the data is stored on a remote server and processed by third-party service providers. With FHE, the data can be encrypted and stored on the server, and computation can be performed on the ciphertexts without the server or the service provider ever knowing the plaintext data.
Due to its unique ability to secure data from end-to-end in all three states, HE has long been dubbed the “Holy Grail of Data Privacy” or the “Holy Grail of Cryptography.”
The idea of HE is not new, and cryptographers first proposed it in 1978. However, they didn’t know at the time if it was possible to achieve. It wasn’t until 2009 when Craig Gentry, then at Stanford, described the first plausible construction for a fully homomorphic encryption scheme, showing that FHE could be realized in principle. Since then, it has been adopted in a variety of areas, including the private and public sectors and academia, where it has been shown to perform at scale.
FHE is perhaps the most important breakthrough in theoretical computer science of the 21st century. Since Gentry’s paper was published, research and implementation efforts throughout academia, government, and industry have brought FHE from theory to reality.
HE enables computations, including machine learning and AI analysis, on encrypted data, allowing data scientists, researchers, and data driven enterprises to gain valuable insights without decrypting or exposing the underlying data or models. This enables organizations to extract value from data while maintaining privacy and complying with applicable regulations. In addition, HE provides a functional and dependable privacy layer, eliminating the trade-off between data privacy and utility. This is particularly useful for enabling collaborations between parties across sensitive data – such as privacy preserving collaborations with patient data between multiple healthcare and research centers, or inter-bank cooperation in financial crime investigations – where different parties can analyze sensitive information without exposing the underlying data to one another.
Because homomorphically-encrypted data is encrypted from end-to-end in all three states, no trusted third parties are ever required. This allows for computations to be outsourced, keeping both the data and the analytical models used to operate on the data safe, secured, and concealed. A cloud host could run a computation on the data, get an encrypted result, and give that result back to the data owner. The data owner could then decrypt that result, with the decrypted result being the same as if they had run the computation on the original data without encryption.
Fully Homomorphic Encryption (FHE) has many potential benefits, but it also has drawbacks that must be taken into consideration.
Benefits:
Drawbacks:
Therefore, FHE has a lot of potential benefits, but it does come with some drawbacks that need to be considered when deciding whether to use it or not.
