As Web3 technologies mature and become more integrated into everyday digital infrastructure, the expectation for privacy is rapidly shifting from a luxury to a necessity. Whether it's securing transactions on public blockchains or enabling machine learning models to operate on sensitive data without direct access, the call for solutions that preserve data confidentiality without compromising performance is louder than ever.
One technology stands out as a potential cornerstone for the next generation of private, decentralized systems:
Fully Homomorphic Encryption (FHE) a form of encryption so powerful that it allows data to be computed on without ever decrypting it.
And Suncreen is leading it. While FHE remains a mystery to many, it could be the “sunscreen” of the digital age: allowing users and developers to operate in full sunlight (i.e., public infrastructure) while shielding sensitive information from exposure.
This article dives deep into what FHE is, how it works, why it matters for blockchain and beyond, and what’s stopping it from becoming mainstream.
What is Fully Homomorphic Encryption?
Fully Homomorphic Encryption (FHE) is a type of encryption that allows for arbitrary computation on encrypted data, producing an encrypted result that, when decrypted, matches the result of the same operations performed on the original data.
In a nutshell, FHE enables someone to perform operations like addition, multiplication, or even machine learning in the dark.
The data stays encrypted throughout the process. No decryption. No leaks. No compromises.
This is fundamentally different from traditional encryption, where data must be decrypted to be used, leaving it vulnerable to breaches during processing.
A Simple Illustration
Let’s say Alice encrypts two numbers, 3 and 5. With FHE:
A third party can compute Encrypt(3) + Encrypt(5)
The result is Encrypt(8), without ever knowing 3 or 5
When Alice decrypts it, she gets the correct result: 8.
That’s the power of FHE. It protects data even during computation a feat traditional encryption simply can’t offer.
Why FHE Matters in Web3
FHE isn’t just an academic curiosity it has real-world applications that align perfectly with the values and demands of the decentralized world.
1. Private Transactions on Public Blockchains
Blockchains like Bitcoin and Ethereum are transparent by design. But this transparency comes at a cost user privacy.
With FHE, it becomes possible to perform financial operations on-chain like sending tokens, participating in auctions, or executing smart contracts without revealing sensitive details such as transaction amounts or bidding values.
Only the intended parties can decrypt and verify the data, while the broader network can still validate the encrypted computations.
2. Confidential Machine Learning
In a world increasingly driven by AI, data is the new oil. But most organizations are reluctant to share their data due to privacy concerns.
FHE enables outsourced machine learning on encrypted data. For instance, a hospital could encrypt patient data, send it to an AI company, and receive model predictions without the company ever seeing the actual data.
This not only ensures privacy but also unlocks collaboration between entities that were previously siloed by data protection laws.
How Does FHE Work? (Simplified)
At its core, every FHE system uses three components:
Key Generation (KeyGen) – Creates a pair of encryption/decryption keys
Encryption – Turns readable data (plaintext) into scrambled ciphertext
Decryption – Recovers the original data from the ciphertext using the key
What makes FHE unique is that it supports two key operations directly on ciphertext:
Homomorphic Addition: Encrypt(a) + Encrypt(b) = Encrypt(a + b)
Homomorphic Multiplication: Encrypt(a) Encrypt(b) = Encrypt(a b)
This allows the construction of complex formulas, logic, or even full AI models over encrypted values. However, each operation adds "noise" to the ciphertext. Too much noise, and the ciphertext becomes undecipherable unless the system uses a process called bootstrapping to "clean" the data during computation.
Under the Hood: How FHE is Built
FHE is constructed using advanced mathematics, particularly lattice-based cryptography a post-quantum secure method that’s resistant to attacks even from future quantum computers.
Depending on the use case, different FHE schemes exist:
Computational ModelUse CaseScheme ExamplesBoolean CircuitsComparisons, logical gatesTFHEModular ArithmeticInteger math, financial transactionsBFV, BGVFloating-Point ArithmeticML, signal processingCKKS
Each scheme comes with its own strengths and weaknesses, making it important to choose the right one based on the specific requirements of the application.
Why FHE Isn’t Mainstream (Yet)
Despite its potential, FHE is not widely used and for good reason. The technology, while promising, faces serious challenges:
1. Usability is Poor
Most FHE libraries are extremely low-level. Using them requires a strong background in cryptography and mathematics. Engineers without this background often find themselves lost in a sea of complex parameters and unexplained behaviors.
As one cryptographer put it, “Working with FHE is like writing in assembly it’s possible, but painful.”
2. Performance is Still a Bottleneck
FHE operations are orders of magnitude slower than unencrypted ones.
Keys can be gigabytes in size
Ciphertexts are massively bloated
Operations like multiplication can take seconds even minutes on modern hardware
While some schemes allow for limited computation efficiently, achieving fully arbitrary computation still comes with a massive cost.
3. Too Many Schemes, Too Little Guidance
There’s a confusing variety of FHE schemes out there, each with different characteristics. Unfortunately, there’s very little accessible documentation explaining the trade-offs between them or guiding developers on when to use which.
Even worse, many schemes are incompatible with each other, making interoperability a major challenge.
What Can Be Done?
The FHE space needs several improvements to become viable for real-world development:
Better Benchmarking
Up-to-date performance comparisons between schemes and libraries are rare. Developers can’t make informed decisions without this data.Simplified Tooling
Libraries must become more developer-friendly. Abstract away the cryptography, provide smart defaults, and focus on usability.Interoperability Across Schemes
Tools like CHIMERA are working on converting between schemes (e.g., Boolean ↔ Modular ↔ Floating-point), but the ecosystem still needs standardization.Hardware Acceleration
GPUs and FPGAs can help. Projects like cuFHE and HEAX have shown performance improvements by orders of magnitude but they’re still niche and under development.
What the Future Could Look Like
Imagine a decentralized exchange where nobody sees your trade size, but every trade is publicly validated.
Imagine DAOs where voting is encrypted, yet results are accurate and trusted.
Imagine AI systems that learn from global health data without compromising patient privacy.
All of this becomes possible with FHE.
It’s not there yet but the foundation is being laid.
Ready to Explore FHE?
For developers looking to experiment, here are a few libraries worth checking out:
Microsoft SEAL
TFHE
PALISADE
Lattigo (Go)
Expect a steep learning curve but also know that you’ll be contributing to one of the most important evolutions in digital privacy.
FHE: A Sunscreen for the Data Economy
FHE doesn’t stop you from using your data. It lets you use it safely just like sunscreen allows you to enjoy the sun without getting burned.
In a world where surveillance capitalism and data breaches are everyday concerns, FHE represents a paradigm shift: total privacy, without losing functionality.
It’s not the future. It’s the future-in-progress and it’s one that engineers, cryptographers, and blockchain builders need to pay attention to now.
About Sunscreen
Sunscreen is a forward-thinking technology company focused on building the foundational infrastructure for privacy-preserving computation using Fully Homomorphic Encryption (FHE). At its core, Sunscreen aims to solve one of the most pressing challenges of our digital era: how to compute on sensitive data without ever revealing it. In a world increasingly dominated by decentralized systems and data-driven applications, the need for strong, mathematically guaranteed privacy is no longer optional it is essential. Sunscreen addresses this need by making FHE practical, performant, and accessible to developers, empowering them to build applications that are both secure and private by design.
The company’s mission revolves around enabling encrypted computation as a native feature in the blockchain ecosystem and beyond. By leveraging FHE, Sunscreen allows data to remain encrypted not only during storage and transmission, but also while it's being processed. This breakthrough enables entirely new possibilities for secure smart contracts, confidential DeFi transactions, private DAOs, encrypted auctions, voting systems, and privacy-preserving machine learning models. Traditionally, these use cases have required a compromise between functionality and privacy. Sunscreen removes that compromise. With the tools and protocols Sunscreen is building, developers can create decentralized applications that operate transparently on public infrastructure without ever exposing user data to the public eye.
What makes Sunscreen especially unique is its deep focus on usability. Historically, working with FHE has required extensive cryptographic knowledge, including advanced mathematics, complex parameter tuning, and performance trade-off management. Sunscreen removes this barrier by abstracting away the technical complexity, providing clean APIs, robust documentation, and developer-friendly tooling that make FHE approachable for everyday engineers not just cryptographers. This approach accelerates adoption and enables the integration of advanced privacy guarantees into Web3 applications without needing to reinvent the wheel.
Beyond technology, Sunscreen represents a philosophical commitment to redefining what privacy means in the decentralized age. Rather than treating privacy as an add-on or afterthought, Sunscreen treats it as a programmable, composable feature of modern blockchain infrastructure. The company’s vision is not simply to protect individual transactions or data points, but to reshape how computation itself is handled enabling systems where encrypted logic becomes the norm. In doing so, Sunscreen paves the way for a new generation of trustless, transparent, yet private applications that uphold user sovereignty, regulatory compliance, and commercial confidentiality.
In a world where surveillance, data exploitation, and transparency trade-offs have become normalized, Sunscreen is creating the tools to let developers and organizations say: yes to decentralization, yes to functionality, and yes to privacy all at once. Sunscreen is, in essence, the sunscreen for your data: shielding it from exposure while still letting it operate under the bright light of decentralized infrastructure.