Data security has become one of the paramount concerns for businesses, governments, and individuals in an increasingly digital world. As data proliferates across cloud infrastructures and shared environments, its privacy and security pose a strategic challenge. To address this, organizations generally depend on encryption at rest (e.g., with an AES 256 key), and encryption in motion (e.g., using TLS 1.2.) Such encryption techniques generally ensure that data remains secure when stored in modern data systems (e.g., databases), or when transferred from a source system to a sink system (e.g., in data pipelines.)

But what happens when the data is in use? For instance, when data resides in memory and is accessed by application processes? Ordinarily, data is decrypted at the time of processing, which may expose it to security risks. This is where Fully Homomorphic Encryption comes into the picture.

What is FHE Computing? How does it work?

Fully Homomorphic Encryption (FHE) is an encryption mechanism that enables computation on encrypted data without the need to decrypt it. Thus, data can stay encrypted forever. While the concept of FHE was first proposed in 1978, the first FHE scheme was proposed in 2009 in a PhD thesis by Craig Gentry of Stanford University. Several variants and newer schemes have been proposed since then. In 2021, DARPA initiated the DRIVE project, which aims to reduce the FHE computational time from weeks to seconds, and this provided a major impetus to FHE computing.

Some of the most prominent FHE schemes today include the Gentry scheme, BGV (Brakerski-Gentry-Vaikuntanathan), CKKS (Cheon-Kim-Kim-Song), and TFHE (Fast Torus FHE). At the core of FHE Computing are cryptographic building blocks that depend on advanced mathematical constructs to achieve homomorphism. Most FHE schemes leverage Lattice-based cryptography as the basis for mathematical transformation.

This goal is to ensure that breaking the encryption becomes computationally infeasible for both classical and quantum computers. Each operation performed on encrypted data increases the noise in the ciphertext. After a certain threshold is reached, the noise makes decryption virtually impossible. A process called Bootstrapping works on reducing the noise in ciphertexts, and enabling unlimited computations on the encrypted data. To improve the practicality and efficiency of FHE, many modern schemes use structured lattices (e.g., Ring-LWE) to reduce the size of ciphertexts, and accelerate homomorphic operations.

FHE Computing is not yet viable for mass adoption.

First and foremost, the algorithms for running computations on encrypted data are extremely slow, thereby rendering FHE computing unviable for most real-world applications. Despite recent innovations, computations on encrypted data take orders of magnitude longer than their plaintext counterparts. Secondly, encrypted data is considerably larger than their original (plaintext) versions, thus necessitating much higher memory and storage needs.

Another major challenge is the significant computational overhead of Bootstrapping. Moreover, integrating FHE into applications is complex—for example, conditional statements need to be implemented using an MUX gate. Most developers may never possess such deep skills, which becomes a major implementation bottleneck.

Finally, FHE performance is closely tied to the selected scheme, and its corresponding parameter set. Each scheme has its own purpose – for instance, BGV is suitable for large-scale computations, TFHE for evaluating boolean circuits, and so on. So, selecting a scheme that supports a variety of needs is not generally straightforward. Additionally, dynamic switching between two schemes is complex and expensive. As a result, the utility of an FHE application is often limited to specific tasks.

Current & Future Direction of Research

As FHE Computing evolves, research and innovation in this field primarily revolve around the following areas:

• Applications in Blockchain technologies, such as secure smart contracts, and privacy-preserving blockchain systems.
• Hybrid encryption models that combine FHE with traditional cryptographic methods to achieve a balance between security and performance – e.g., leveled homomorphic encryption.
• Integration of FHE with specialized hardware accelerators, including FPGAs and ASICs – for instance, Intel and Galois are reportedly developing FHE-centered chips.
• Performance optimization through efficient Bootstrapping techniques (e.g., lazy bootstrapping), innovations in modern schemes (e.g., CKKS), and techniques that reduce computational overhead (e.g., batching.)
• Quantum-resistant cryptography, particularly to improve the capabilities of lattice-based methods that are generally resistant to quantum attacks.

Closing Comments

As data becomes the new currency in this AI age, safeguarding sensitive information while still enabling its utility has become one of the most pressing challenges. FHE computing has the potential to radically transform things, with far-reaching effects on privacy-preserving machine learning, secure cloud computing, secure financial transactions, and other areas. Its ability to process encrypted data will likely revolutionize every sector, particularly the highly regulated ones like healthcare and finance. While current challenges limit its widespread adoption, ongoing research is making significant strides in paving the way for real-world implementations in the coming years.

PS: 10-15% of this article was written with the help of Generative AI.