Introduction to Fully Homomorphic Encryption (FHE): What It Is and Why It Matters?

This is the second article in our series on privacy-enhancing technologies. In the first article, we explored Zero-Knowledge technology, and in this one, we will take a deep dive into Fully Homomorphic Encryption.

What is Fully Homomorphic Encryption (FHE)?

Fully Homomorphic Encryption (FHE) allows computations on encrypted data without ever needing to decrypt it. FHE opens up new possibilities for secure data analysis and processing in healthcare, finance, privacy-preserving machine learning, etc.

Let's try to understand Fully Homomorphic Encryption (FHE) with the analogy of a magical lockbox: Imagine you have a special notebook containing sensitive information, locked inside a magical box secured by a unique key. Unlike a regular safe, this box is extraordinary — you can ask it specific questions about the notebook's contents without ever opening it. For instance, you might ask, "What is the sum of all numbers in the notebook?"

The magical box can perform calculations directly on the locked notebook. It does complex math like addition or multiplication while the notebook remains completely sealed. When you request the result and provide the magical key, the box reveals the answer. The remarkable part is that throughout this entire process, the notebook stays locked and the original information remains completely private and untouched.

You might be wondering — where exactly is this magical box stored, and who provides the technology that lets me lock my data inside it? In the world of FHE, the "magical box" takes the form of software, hardware, or cloud infrastructure designed and maintained by experts in the field. These developers and service providers are the ones who make it all possible, enabling individuals, organizations, and even governments to securely store and process encrypted data. Think of them as the creators of this remarkable box, providing the tools that turn privacy-preserving computation into a reality for real-world applications.

Importantly, this magical box doesn't have to reside on a public ledger. In fact, the "magical box" is implementation-agnostic; it can exist on-premises, within private infrastructure, or in secure cloud environments tailored to specific needs.

Just as this magical box lets you learn about your notebook's contents without compromising its security, Fully Homomorphic Encryption allows computers to process encrypted data without decrypting it, ensuring absolute data privacy during computational tasks.

What is the potential of FHE? What are the use cases?

FHE has profound implications across numerous domains, each underscoring its importance for both individuals and organizations:

• Confidential Voting: FHE has the potential to revolutionize electronic voting systems. With FHE, votes can be encrypted before being submitted, allowing election officials to tally results without accessing the content of individual votes. This approach offers several key benefits: complete privacy, ensuring individual ballot choices remain confidential; robust system integrity, preventing vote tampering; and verifiability.
• Decentralized Identity: By encrypting identity attributes, FHE allows people to selectively share specific details about themselves without compromising their overall privacy. Users can prove essential claims such as age, nationality, or professional credentials while maintaining complete control over their sensitive personal data.
• Privacy-preserving AI: FHE could enable the secure training of machine learning models directly on encrypted data, allowing organizations to harness valuable insights from sensitive datasets without compromising privacy. By performing computations on encrypted data, confidential machine learning ensures that private information, such as healthcare records remains hidden throughout the training process.
• Confidential DeFi: FHE could transform blockchain-based financial systems by enabling transactions, smart contract execution, and complex financial computations to be performed entirely on encrypted data. This ensures that sensitive financial information, such as transaction details, user balances, and contract interactions, remains confidential. By encrypting every aspect of the process, FHE establishes a secure foundation for truly private and decentralized financial services, addressing key privacy concerns while maintaining functionality.

Call for FHE Startups and Experts: We're looking to engage with startups and experts in Fully Homomorphic Encryption (FHE) technology. If you're pioneering advancements in FHE, we encourage you to reach out. Our vision is a future where data privacy and security are fundamental. If you're passionate about driving this mission forward, we're eager to collaborate. Connect with us on LinkedIn or reach out via email at pitch@blockwall.vc.

How Fully Homomorphic Encryption (FHE) Works?

1. Encryption - Securing Your Data: FHE protects sensitive data by transforming it into a secure, unreadable format. This encryption allows computational operations while keeping the original information completely hidden, with only authorized parties able to access the data using the specific decryption key.
2. Performing Operations on Encrypted Data: FHE's unique capability is performing mathematical operations directly on encrypted information. For instance, with two encrypted numbers like 5 and 10, the system can add them without first decrypting the values. The operation is conducted using special encrypted transformations that maintain the data's confidentiality throughout the computation.
3. Getting Encrypted Results: After completing the mathematical operation, the output remains encrypted. Using our previous example, the result of adding 5 and 10 would be 15, but this result is still locked and unreadable to anyone without authorization.
4. Decryption - Unlocking the Final Result: When needed, the authorized user can apply the decryption key to reveal the actual result. In our example, this would finally expose the number 15, completing the secure computational process.

Here are some companies that are actively innovating in the FHE domain:

• Zama, a leading provider of FHE-based solutions, raised $73 million Series A funding round in March 2024, solidifying its position as a pioneer in the field. One of Zama's standout innovations is the fhEVM, which enables the execution of confidential smart contracts directly on encrypted data. This breakthrough guarantees both data confidentiality and the composability essential for complex blockchain applications.
• Inco is developing the Confidential ERC20 Framework, a pioneering initiative powered by FHE. This framework aims to integrate FHE into ERC20 tokens, enabling a wide range of confidential use cases across decentralized finance (DeFi).
• Fhenix is an FHE-powered Layer 2 rollup designed for the Ethereum ecosystem. By utilizing the fhEVM, it enables secure and private interactions while maintaining compatibility with Ethereum's infrastructure.
• Lattice AI provides a solution where AI models can process and respond to encrypted queries without needing to decrypt them first. Built entirely on homomorphic encryption, this technology ensures that data remains encrypted at all times, with only the client holding the decryption key. This design guarantees that sensitive information stays private and secure throughout the process.

Areas of Improvement for FHE

1. Threshold FHE: This method divides the decryption key into parts, sharing them among multiple parties, so only a subset is needed to decrypt the data. The main improvement needed is to make this process more scalable and efficient, as current implementations can be slow due to the high communication and computational demands during decryption.
2. Verifiable FHE: Verifiable FHE ensures that a party can prove to a data owner that computations on encrypted data were done correctly without revealing the data itself. The key improvement needed is reducing the overhead in verification processes, such as minimizing proof size and improving verification speed, while still maintaining strong security.
3. New Schemes and Implementations: Ongoing research is focused on developing new FHE schemes that are more efficient and flexible. Improvements are needed to balance the complexity and performance of these schemes, enhance security against potential future attacks, and extend capabilities to handle more complex data types.
4. Acceleration: FHE operations can be slow, which calls for a focus on speeding them up through hardware optimization and algorithmic improvements. Key areas for enhancement include developing specialized hardware like GPUs and optimizing homomorphic operation performance to make them faster and more efficient.

Summary

Fully Homomorphic Encryption (FHE) works by encrypting data, performing mathematical operations directly on the encrypted information, and producing encrypted results that only authorized users can decrypt. It has transformative applications in areas like healthcare, decentralized identity, privacy-preserving AI, and secure decentralized finance (DeFi). Ongoing advancements focus on improving scalability, efficiency, and performance through hardware optimization and new encryption schemes. FHE has the potential to redefine secure data processing, making it vital for organizations seeking privacy and security in computational tasks.

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