Introduction to FHE and Blockchain: Implications for Scalability and Privacy

Introduction

Fully homomorphic encryption was first envisioned by Ronald Rivest, Adi Shamir, and Leonard Adleman in 1978. However, due to major computational limitations and its complexity, FHE wasn’t practically implementable and lacked real-world application for years, existing exclusively in theoretical discussion. It was only with advancements in computing and more efficient algorithms that FHE began to see potential real-world applications. FHE began reaching the mainstream after Craig Gentry introduced the first feasible FHE scheme in 2009, and since then, the technology has received ever-increasing attention. With its ability to allow for encrypted data to be arbitrarily manipulated by many while only decryptable by the data’s private key holder(s), FHE has the potential to transform countless industries, such as social media, military communications, and blockchain. Major tech companies such as Google and Meta are spending on R&D to comply with ever-stringent privacy and data protection laws. Likewise, governments are spending on R&D to protect themselves from cybersecurity threats. More recently, FHE has entered blockchain discussions as a means to provide privacy to crypto users. FHE-enabled blockchains allow all on-chain data to be made private, even from validators (countering MEV).

How FHE Works

Fully homomorphic encryption allows for the unlimited arbitrary manipulation and computation of encrypted data without having to reveal that data. Imagine that there is a box that anyone can reach into, but only one person with the key to the box can open it. Anybody can reach in and manipulate the contents of the box, but they cannot take those contents out, see the contents, or open the box. That is essentially how FHE works — it allows for data to be manipulated without that data having to be revealed.

The World Needs FHE More Now Than Ever

With ever-increasing digital risks to privacy leaks, it is clear that FHE is needed now more than ever. This need for FHE is only growing as more businesses and individuals move their data storage and computing to cloud-based solutions. However, FHE’s lack of standardization thus far has hindered its potential, making it challenging to harness its full capabilities in practical applications.

Although homomorphic encryption has primarily existed as a theoretical concept until recently, leading HE schemes are gradually being integrated into real-world systems that require privacy-protected computations. Furthermore, the complex security properties of homomorphic encryption schemes can be very technical and hard to grasp, even for experts in the field. Comprehensive standardization of HE would demystify these properties, helping to present them clearly and comprehensibly. Clarity around HE is crucial for broader understanding and real-world application of the technology, especially as we navigate the intricacies of data privacy in an increasingly interconnected world.

FHE is increasingly being adopted in areas like cloud computing, where it enables the processing of encrypted data without revealing its contents, helping to ensure privacy and security. In healthcare, FHE facilitates the confidential analysis of medical records, permitting research while protecting patient privacy. Additionally, FHE is gaining traction in government and defense for its usage in secure communications and data sharing, as well as in AI and machine learning for training models on sensitive data without sacrificing privacy. As FHE technology matures, its usage is expected to expand across more industries (such as crypto), driven by the rising need for secure data processing and privacy preservation.

Types of Homomorphic Encryption (HE)

There are multiple types of homomorphic encryption. A breakdown of the various sorts is provided below:

Partial Homomorphic Encryption (PHE)

• The most basic form of HE
• Allows for either adding or multiplying an encrypted data set
• Additive or Multiplicative HE are types of PHE

Somewhat Homomorphic Encryption (SHE)

• Best for simpler computations that aren’t too operationally-intensive
• Allows for both addition and multiplication of the encrypted data

Leveled Homomorphic Encryption (LHE)

• Ideal for applications where the complexity of computations is known in advance and does not require the flexibility of FHE
• Allows for a predetermined number of both additions and multiplications on encrypted data
• Unlike FHE, LHE does not require bootstrapping (a process for controlling noise in encrypted computations), but it has a fixed limit on the depth of operations it can perform

Fully Homomorphic Encryption (FHE)

• Enables unlimited arbitrary computations of encrypted data
• Complicated and pricey to implement; requires a lot more computational resources than PHE and SHE

FHE vs ZK

Blockchains have been plagued by troubles pertaining to scalability and privacy since their origin. More recently, zero-knowledge proofs (ZKPs) have gained popularity in crypto as a potential solution to these challenges. ZKPs enable users to prove knowledge of a value without revealing that value. This technology opens the door for all types of applications; for example, a bartender could verify that an individual is of legal age to purchase a drink without the individual needing to expose all of their ID card’s other information to the bartender, such as their weight or their home address.

In blockchain solutions, ZKPs have thus far been the go-to answer in privacy tooling. However, ZKPs require trusting third parties with private data, which is not ideal for crypto privacy. This is because ZK rollups have users send transactions to centralized sequencers that generate proofs. Thus, ZKPs are not ideal for apps that require private and global states simultaneously.

ZCash pioneered using ZKPs for private crypto payments, and its computations can be handled by laptops and phones. This was the first major ZKP use case in crypto. Now, a few years later, it appears that ZKPs are a better solution for scaling than privacy. ZK rollups and Zero-Knowledge Machine Learning (ZKML) are crypto use cases utilizing ZK technology gaining popularity.

Below is a comparison chart between FHE and ZKPs.

Challenges of FHE

FHE does not come without its challenges. FHE is notably more computationally intensive and slower than other cryptographic methods like ZKPs, making it less practical for some real-world applications. The complexity of key management, in addition to the need for effective noise management, further adds to its limitations. Thus far, FHE’s practical use cases are limited due to these challenges, and the lack of standardization and accessible public tooling slows its broader adoption. To address these issues, technical solutions such as threshold decryption schemes using Multi-Party Computation (MPC) have been proposed to distribute decryption keys among multiple nodes, thereby avoiding the risks associated with single-entity control. For wider adoption in the crypto sector and beyond, there is a pressing need for simpler implementation methods, along with the development of more user-friendly tools and Software Development Kits (SDKs) that can democratize access to this powerful encryption technology.

Combining FHE and ZKPs

Although FHE enables the processing of encrypted data, it is incapable of verifying the correctness of user inputs and computations; therefore, leveraging FHE and ZKPs together enhances efficiency and cost-effectiveness, especially when incorporated with co-processor technology. FHE enables computations on encrypted data, preserving confidentiality, while ZKPs enable verification of these computations without revealing the underlying data. This combination heightens data privacy and security while addressing challenges like computational intensity and reliance on third parties. Co-processors play a huge role in this integration, efficiently processing and validating encrypted data. They leverage the combined strengths of FHE and ZKPs to enable validators to execute computations and generate proofs of correctness, thereby offering a stronger privacy-preserving computational environment than using either one solution alone.

This integration not only improves security in blockchain applications but also expands their potential use cases. Co-processors permit complex operations that were previously impractical or impossible due to computational limitations and/or privacy concerns. By enabling trustless and efficient validation processes, co-processors with FHE and ZKP capabilities can tackle longstanding issues in the blockchain space. This approach is especially promising for applications requiring high data privacy, such as in finance, healthcare, and secure communications, denoting a significant leap forward in the evolution of blockchain technology.

Applications In Crypto

FHE has the potential to revolutionize the crypto landscape in many ways. FHE use cases in crypto include:

As FHE is beginning to gain traction in crypto use cases, teams are utilizing the technology in the projects they build. The following table provides an overview of many significant players and projects in the FHE domain. From middleware and infrastructure to Layer-1 blockchains and dApps, these entities are at the forefront of integrating FHE into the blockchain ecosystem.

Value Accrual

In the rising field of FHE, technology providers like Zama are positioned to capture the most value, compared to the providers of applications and tooling that leverage FHE technology. As leaders in this specialized area where blockchain and FHE meet, companies such as Zama can establish market leadership through their first-mover advantage and advanced technologies that they provide to other protocols. This not only sets them up to gain a dominant market share but also opens paths for lucrative licensing opportunities with other businesses.

Additionally, the ever-increasing global emphasis on data privacy and security amplifies the demand for FHE solutions, further enhancing the financial prospects of these technology providers. Early movers in this space, like Zama, can leverage partnerships and collaborations with larger tech entities, potentially integrating their solutions into existing platforms to reach wider markets. This, coupled with their potential for establishing a competitive advantage, positions them to attract significant investment and lucrative deals, thereby capturing substantial monetary value in the FHE landscape.

Closing Thoughts

In conclusion, Fully Homomorphic Encryption (FHE) is a groundbreaking development in cryptography, offering newfound levels of data privacy and security. As it matures and prevails over its current limitations, such as computational intensity and the need for standardization, its potential applications are immense. With the growing demand for data privacy and security, the role of FHE in transforming how we handle sensitive information in a digitally interconnected world cannot be overstated. The advancements in this field hold significant implications for data privacy and security in our increasingly digital world and are poised to revolutionize blockchain and many other industries forever.