FHE (Fully Homomorphic Encryption) is an advanced encryption scheme that enables direct computation on ciphertexts, without the need for decryption. This approach ensures that sensitive data remains encrypted throughout the entire processing cycle, with all computations yielding encrypted results that protect the underlying plaintext data. Essentially, its encryption for data in-use, completing the trifecta of useful encryption methods (the two we know being for data at-rest and in-transit).
This property of FHE is useful in scenarios where privacy is a concern, such as in cloud computing, where third-party service providers must process sensitive information and user data. Through FHE computation, 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. The confidential data remains protected throughout all processing stages, ensuring privacy during machine learning and data analysis tasks.
FHE has earned its reputation as the “Holy Grail” of data privacy through its ability to secure sensitive information in all three states. This homomorphic encryption scheme represents a fundamental breakthrough in lattice-based cryptography and data security.
While the concept of homomorphic operations emerged in 1978, the first viable fully homomorphic encryption scheme wasn’t realized until 2009. Craig Gentry’s groundbreaking work with ideal lattices at Stanford provided the mathematical proof that FHE was achievable. Since then, this homomorphic scheme has been implemented across private and public sectors, demonstrating valuable insights at scale.
FHE is perhaps the most important breakthrough in theoretical computer science of the 21st century. Since Gentry’s original publication, extensive research and implementation efforts throughout academia, government, and industry have brought FHE from theory to reality.
The homomorphic evaluation capabilities enable complex computations, including machine learning and logistic regression, on encrypted data. This allows organizations scientists, researchers, and data-driven enterprises to extract valuable insights while maintaining privacy and regulatory compliance. FHE’s functional and dependable privacy layer eliminates the traditional trade-off between data utility and privacy, enabling secure collaboration on sensitive data – such as healthcare research or financial investigations – where multiple parties can analyze confidential information without exposing the underlying data to one another.
The end-to-end encryption of homomorphic computational threads eliminates the need for trusted third parties. This allows for computations to be outsourced to cloud providers, keeping both the data and the analytical models used to operate on the data safe, secured, and concealed. A cloud host can process the data, generate an encrypted result, and return it to the data owner. Using their decryption key, the owner can decrypt the result, which matches the outcome of performing the same computation on the original, unencrypted data.
Fully Homomorphic Encryption (FHE) has many potential benefits, but it also has drawbacks that must be taken into consideration.
Enhanced Privacy: Since FHE allows computations to be performed on encrypted data directly, it can provide a higher level of privacy compared to traditional encryption methods.
Comprehensive Security: Homomorphic encryption can protect plaintext elements both in rest and in transit, effectively reducing the risk of data breaches.
Cloud Accessibility: FHE could make cloud-based computation significantly more accessible and secure, as personal data would not need to be entrusted to third parties.
Processing Efficiency: FHE can enable efficient processing of large amounts of data without the need to decrypt and re-encrypt the data, reducing computation time and resources.
Increased Computational Overhead: FHE computation typically requires more processing resources than traditional encryption methods. However, advancements in computing infrastructure and algorithm optimization continue to reduce this gap.
Implementation Complexity: Developing and implementing effective FHE algorithms is complex and requires significant expertise in cryptography and mathematics. Duality’s solutions address this challenge by offering ready-to-deploy tools that simplify adoption.
Emerging Industry Adoption: FHE is still a relatively new technology, so it may take time for it to be widely adopted in specific industries.
Key Management: Managing decryption keys and random noise elements with FHE is a complex task, and any mistake could lead to data loss or compromise. Duality provides support and tools to ensure secure key management for all users.
Learn how our advanced FHE solutions can secure your sensitive data while enabling powerful computation capabilities. Contact us to discover more.
