Cryptography in the Face of Quantum Threats: Understanding Fully Homomorphic Encryption, 2026/03/18 23:59:59

In contemporary cryptography, there are encryption methods that enable data protection without limiting processing capabilities. One such method is Fully Homomorphic Encryption (FHE). FHE is highly resistant to quantum attacks.

What is Fully Homomorphic Encryption

Imagine a scenario where you wish to send important data to a remote server for processing, but for specific reasons, you prefer not to disclose the contents of that data. How does this interaction typically occur? The server must first decrypt the information received from you, perform the necessary computations, and then return the result. This means that all content is exposed—exactly what you wanted to avoid.

Homomorphic encryption offers a different approach. It allows certain mathematical operations to be performed directly on encrypted data. After decrypting the result, the user receives the outcome of the computations as if the operations were conducted on the original plaintext data. In other words, calculations occur without revealing the data itself.

The term “homomorphism” originates from mathematics and refers to a mapping that preserves the structure of operations. In cryptography, a homomorphic form of encryption is one where the result of an operation on encrypted data corresponds to the result of the operation on the original data.

Does it sound somewhat complex and seemingly impossible? Let’s clarify with simplified examples.

Simplified Examples

There are several greatly simplified and abstract examples/analogies that can help explain the principle of homomorphic encryption and the problems it addresses.

A Gem and Inaccessible Data

Suppose you possess a precious gem and wish to send it to a jeweler for processing. For instance, you want to change its shape or add facets to incorporate it into a piece of jewelry, like a ring. The gem is rare and valuable, and you are concerned that the jeweler might substitute it with a fake or even steal it. What if, after processing, you receive a counterfeit? How can you have the gem processed by the jeweler without handing it directly to them?

It seems impossible. However, we can use a special transparent box, the key to which only you possess. You place the gem inside this container, which has tightly secured, highly sensitive gloves attached to its walls. Theoretically, the jeweler can insert their hands through the glove openings and work with the gem. In this way, the jeweler does not have direct access to the material and cannot steal or substitute it. Once the work is completed, you receive the sealed container in its entirety.

In cryptography, with homomorphic encryption, instead of the gem, we process encrypted data without revealing it—performing mathematical operations on the ciphertext. But how can one compute with unknown data?

Flower Sales and Altered Data

Another simplified example can be provided. Let’s say Alice sells flower bouquets. Each bouquet has a different price, and over the year, she has sold several thousand bouquets. Now, Alice wants to find out her average monthly earnings from these sales. To do this, she turns to her acquaintance, accountant Bob. However, Alice does not want to disclose the actual prices of the bouquets and her total income to Bob.

Therefore, she applies a simple transformation. Before sending the data to Bob, Alice multiplies the price of each bouquet by two. She sends the resulting values to the accountant. Bob adds all the numbers received and divides the total by twelve to calculate the average monthly income. However, the result is twice the actual value since all the original data was “encrypted” by multiplication beforehand. Once the result is sent back to Alice, she simply divides it by two to obtain the average monthly figure.

Types of Homomorphic Encryption

The concept of building a system based on Fully Homomorphic Encryption (FHE) was proposed in 1978 by researchers working on the RSA cryptographic algorithm: Ronald Rivest, Leonard Adleman, and Michael Dertouzos.

Unfortunately, constructing FHE was not feasible at that time. However, as time passed, research progressed. Ultimately, two types were identified, differing based on the mathematical transformations involved:

• Partially Homomorphic Encryption. This type allows for one operation on encrypted data: either multiplication or addition. Notably, the RSA algorithm is homomorphic with respect to multiplication.

• Fully Homomorphic Encryption. Unlike the previous type, this supports both addition and multiplication operations.

A significant breakthrough in FHE research occurred in 2009 when Craig Gentry, a researcher from Stanford University, published a paper on the subject. He effectively proposed a variation of fully homomorphic encryption based on lattice-based cryptography—a specific area of cryptography. In simpler terms, it became clear that such a form of encryption was indeed possible.

The challenge with FHE was the increase in “noise.” Each operation on the ciphertext raises the so-called “cryptographic noise”—a necessary component of encryption for the security of the scheme. When the noise level exceeds a certain threshold, correct decryption of the result becomes impossible. Gentry’s cryptosystem proposed a solution—re-encryption after a certain number of operations. The mechanism for recalibrating the ciphertext to reduce noise levels is known as bootstrapping. However, this solution was not perfect, leading to multiple refinements since then. Following Gentry’s work, more efficient cryptosystems like BGV, BFV, and CKKS emerged, which are currently utilized in cryptographic developments.

Due to the existing challenges, another type of concept is sometimes encountered—limited homomorphic encryption, where the number of operations is constrained by noise growth. Although it allows for several operations, unlike the partial type, it still has limitations—unlike FHE. This type is often referred to as a precursor to Fully Homomorphic Encryption.

FHE and ZKP

Fully Homomorphic Encryption shares some conceptual overlap with another cryptographic concept—zero-knowledge proof (ZKP) technology. Without delving into specifics, it can be stated that ZKP protocols aim to prove ownership of valid information without disclosing it. In the case of FHE, data is encrypted for subsequent transmission for computations and transformations. While the overarching idea is to keep sensitive information undisclosed to outsiders and enhance overall privacy, their objectives differ.

Another important similarity is the potential application of both concepts within blockchain systems, which are inherently decentralized and lack complete trust among participants—instead relying on consensus. Overall, these are different solutions for different problems that could hypothetically complement each other.

FHE vs Quantum Threat

Although FHE is regarded as a highly promising area of development, there are currently not many standards associated with this type of encryption—unlike other cryptographic approaches.

Interestingly, due to its characteristics, FHE demonstrates significant resilience against the threat of quantum computing. A key factor in this is the aforementioned lattice-based cryptography, which represents a robust method of post-quantum cryptography. Consequently, interest in FHE is growing. Hypothetically, such a system, in conjunction with ZKP (or independently), could be utilized in the crypto market. Examples of applications include:

• KYC/AML Procedures (“Know Your Customer”/anti-money laundering)—without disclosing identity, but confirming compliance with all requirements.

• Enhanced Privacy. Opportunities arise to encrypt user data within a specific blockchain network, storing and processing this information in encrypted form.

• Improved Voting Model. When making important decisions through voting in blockchain projects, data can be transmitted and processed in encrypted form.

• Cloud Computing and Machine Learning. With FHE, it becomes possible to send information for processing to cloud services without decrypting the information itself. Similarly, machine learning can be conducted on encrypted data.

Project Examples

One of the most notable participants in the crypto industry focusing on FHE is Zama. The primary technology being developed by this project is FHEVM. This solution is compatible with the EVM ecosystem and, according to the developers, enhances the privacy of smart contracts and dApps (decentralized applications).

At the end of 2025, the Shiba Inu meme coin development team announced a collaboration with the Zama project and the future integration of fully homomorphic encryption into the Shibarium blockchain. Shiba Inu marketing specialist Lucie stated that by 2026, this would ensure complete confidentiality of smart contracts within the blockchain.

Conclusion

The concept of fully homomorphic encryption allows for computations on encrypted data without decryption. This could transform approaches to privacy and security. The technology opens up possibilities for cloud services, blockchain projects, and machine learning, where maintaining data confidentiality is critically important. However, the implementation of FHE has not yet achieved widespread adoption in the crypto industry.