Fully Homomorphic Encryption
Altering the Fabric of Secure Computing
In the ever-evolving landscape of digital technology, old concepts once thought to be infeasible become—well—feasible. Many of these concepts bring forth transformative utility and productive unlocks to our industry. In the area of cryptography, one such concept is fully homomorphic encryption (FHE). While the concept of FHE has been around for years, computational constraints and complexity have limited its practical application. Until now.
When we think about server-side vulnerability over the last few decades, almost every exploit can be attributed to the unfortunate fact that some sensitive piece of information was left unencrypted. While the solution seems simple — don’t ever decrypt data and make sure no encryption key is ever stored on any server — such a solution has not been practically possible before now. Today’s advances in computing and cryptography have made it possible to leverage the full capabilities of FHE. This has and will continue to have a monumental impact on the security and privacy of many areas of technology — including that of a blockchain.
FHE: The Mechanics
To put it simply, FHE allows computation to be performed on encrypted data. This means that a third party, such as a cloud server or blockchain node, can execute operations on encrypted information without learning anything about the underlying data. FHE works by transforming plain data (plaintext) into encrypted data (ciphertext) using an encryption key. Once encrypted, the ciphertext can still undergo computational operations while maintaining its private form. This is the key innovation of FHE: these operations will correspond directly to the same operations on the plaintext, even though the server or processor handling the data cannot see the original content.
FHE Mechanics (source: Gregory Boland | LinkedIn)
At the heart of FHE are complex mathematical structures (such as lattice-based cryptography) that enable these encrypted operations to preserve the relationships between the encrypted values. When the encrypted result of a computation is later decrypted by the data owner (who holds the decryption key), it yields the same output as if the computation had been performed directly on the unencrypted data. Hence, both functionality and privacy are preserved.
The benefit here is clear. FHE is particularly valuable for privacy-preserving applications because sensitive data can be processed without exposure. Because server data doesn’t require decryption to be utilized, it promises less vulnerability and exploits. In blockchain technology, this means enabling smart contracts and dApps to work on encrypted data without exposing sensitive information. This improves overall security and introduces new possibilities for privacy-focused applications, such as private voting and secure multi-party interactions.
Moving Beyond Theory
The concept of fully homomorphic encryption has been present in academia for decades, but until recent advances in technology, it has been too computationally intensive to be practically useful.
Advances in cryptography that have made FHE possible are rooted in breakthroughs in mathematical techniques and algorithms, particularly in the field of lattice-based cryptography. Traditional encryption methods, while secure for storing and transmitting data, were not capable of performing computations on encrypted information. This changed with a series of critical innovations.
One major development came from Craig Gentry in 2009, who introduced the first viable FHE scheme. Gentry’s breakthrough involved using lattice-based cryptography and the concept of "noisy" encryption. The core challenge in FHE is managing the noise that accumulates as operations are performed on encrypted data. Gentry’s work introduced a way to periodically reduce this noise using the technique of bootstrapping. In this context, bootstrapping involves re-encrypting the ciphertext partway through computations, which prevents the noise from overwhelming the encrypted data and allows additional operations to be performed. This was a novel solution at the time.
From here, we saw the development of more efficient homomorphic encryption schemes. Early FHE implementations were impractically slow, but research in cryptography has since optimized these algorithms to improve their performance. Techniques like packing multiple data bits into a single ciphertext and the use of structured lattices have significantly reduced the computational overhead of FHE. In addition to algorithmic improvements, hardware acceleration has played a role in making FHE more feasible. Researchers have developed specialized hardware and optimized processors designed to accelerate homomorphic computations. This addresses the performance bottleneck that initially limited FHE’s use in real-world applications.
Together, these advancements have made fully homomorphic encryption possible and increasingly practical. As a result, they have opened new possibilities for privacy-preserving computation in various fields like cloud computing, machine learning, and blockchains.
Super Encryption for the Blockchain
FHE revolutionizes blockchain security and privacy in a myriad of ways. As a result, innovation in this area will likely be a focal point for many emerging projects who view it as an essential solution to strengthen the security of on-chain applications. There are several notable cases for why FHE will be increasingly valuable.
Confidentiality Onchain
FHE empowers users to decide which assets to keep confidential on the blockchain, facilitating true on-chain privacy across various applications. This selective privacy is critical, especially in public blockchain networks where transparency is typically prioritized but user confidentiality can be compromised. With FHE, individuals and organizations can maintain control over which data remains encrypted, allowing them to disclose only the information necessary for specific transactions or processes while keeping sensitive data hidden from public view and unauthorized parties.
Consider DeFi applications in which users engage in complex transactions. Ensuring proper on-chain encryption allows users to keep their financial details secure and private in these transactions. Transactions can still be executed in real-time, but user privacy is preserved without sacrificing the trust and verifiability that decentralized systems require. Or consider the healthcare sector. Medical records and personal health data can be processed and analyzed on-chain without revealing the actual data, ensuring both patient confidentiality and the integrity of healthcare services conducted over blockchain networks.
Security in dApps
Another important area where FHE enables privacy-preserving computations is in dApps. The cryptography scheme allows developers to build systems where data confidentiality is maintained throughout the lifecycle of a smart contract. This expands the scope of use cases/dApps that can be safely deployed on the blockchain. Consider sealed-bid auctions, where participants’ bids remain confidential until the auction is complete, or voting systems where votes are encrypted yet counted correctly and securely without revealing voter choices. The ability to perform these operations on encrypted data fundamentally alters the privacy landscape of blockchain. Previously, these privacy-preserving functionalities would often have required off-chain solutions or complex zero-knowledge proof mechanisms, which often add computational overhead.
Multi-Party Computations
Another potential impact area for FHE is secure multi-party computation (MPC). In decentralized ecosystems, multiple parties often need to collaborate on a shared computation, such as a joint decision-making process or collaborative data analysis. FHE enables these computations to occur in a trustless manner, where each party’s inputs remain encrypted, but the collective computation still produces a valid and useful result. This is particularly valuable in industries like supply chain management, where various participants need to interact and exchange sensitive data, yet no single party should have complete access to the other's information.
Decentralized Key Management
In addition to privacy, FHE strengthens security by allowing computations to happen without exposing data to the risk of interception, tampering, or unauthorized access. Decentralized key management, enabled by FHE, ensures that decryption capabilities are distributed among multiple validators. This prevents any single entity from gaining exclusive control over sensitive data. This decentralization aligns well with blockchain's core philosophy of trustless, permissionless systems, and further enhances the overall security architecture.
In sum, FHE transforms blockchain privacy and security by giving users granular control over their data, enabling private on-chain computations, and decentralizing key management. These advancements not only safeguard sensitive information but also expand the range of blockchain use cases. Going forward, we can continue to make privacy-preserving dApps more secure and scalable across all sectors.
The Computing Space as a Whole
Taking a step back, it seems plausible to argue that the benefits of fully homomorphic encryption will translate to the computing sector at large. What's more, it seems conceivable—likely, even—that FHE abilities will revolutionize the entire Web2 space in addition to Web3’s. I strongly suspect that many Web2 applications will soon be FHE-enabled. In this case, FHE stands to alter the very fabric of secure computing.
As FHE matures and its performance bottlenecks are reduced, Web2 applications will likely begin adopting this encryption model to ensure that data remains private throughout its lifecycle, even when processed by third parties. Introducing FHE fully into Web2 would mark a fundamental shift in secure computing. Imagine an ecosystem where companies like Google, Amazon, and Facebook no longer need to access the raw data of their users to deliver personalized services, run machine learning models, or analyze user behavior. In such a scenario, encrypted user data could be processed on company servers without any risk of exposing the actual content, reshaping the trust dynamic between users and service providers.
These benefits naturally extend to cloud computing as well. Today, businesses often face a tradeoff between the cost-efficiency/scalability of cloud services and the security concerns of sending sensitive data to external servers. With FHE, this tradeoff would disappear, as encrypted data could be processed in the cloud without decryption, allowing organizations to fully embrace cloud computing without compromising privacy.
When it comes to machine learning and AI, the widespread adoption of FHE in both Web2 and Web3 could accelerate advancements in the space. Currently, training machine learning models on sensitive data (such as medical records or financial information) requires careful anonymization techniques and data access controls. FHE may allow for encrypted datasets to be used directly, offering a way to build powerful AI models without compromising data privacy. This would not only open new doors for AI development but would also alleviate concerns around data sharing and collaborative research.
AI Computation on Encrypted Data (source: ChainLink)
Regardless of the path forward, many industries stand to be disrupted across both the Web2 and Web3 spaces. Particularly in Web3, with today’s increasingly powerful blockchains we’ll continue to see the emergence of more capable dApps requiring robust privacy, further cementing FHE’s use cases across industries far and wide.
Going forward, full privacy and security will not be luxuries—they’ll be the very foundation of the digital world. The future is in FHE, and the future is fully encrypted.