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Encryption does a good job of protecting data when it’s stored or in transit, but not when it’s actually being used. That’s a problem, especially as more companies rely on AI tools that need access to sensitive information.
Homomorphic encryption (HE) offers a way around this. Instead of decrypting data to run a computation, it lets you work with the encrypted version and still get a useful result.
It’s been a promising idea for years, and in 2025, it’s finally starting to become something developers can actually use.
Key Takeaways
- Homomorphic encryption allows data to be processed without ever being decrypted.
- It protects data at rest, in transit, and during use.
- Fully homomorphic encryption supports unlimited operations on encrypted data.
- Performance remains a major challenge, but hardware and standards are improving.
- Developers can start small using libraries like SEAL or OpenFHE for secure AI workflows.
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Homomorphic Encryption Explained Simply
You send encrypted data to a system like a cloud server, and that system can run operations on the ciphertext. When you decrypt the result on your end, it’s the same as if you had run those operations on the original data.
This ability means sensitive information can stay protected even during processing. It’s a major shift from how most systems handle data today.
Regular Encryption Just Isn’t Enough Anymore
Traditional encryption protects data in two main states: at rest (stored on a disk) and in transit (moving across a network). But once you need to use that data, for example, to run a machine learning model or generate analytics, you usually have to decrypt it first. That’s where the risk comes in. If someone gains access during processing, they can see the raw data.
Homomorphic encryption fixes that gap. It keeps the data secure not only when it’s stored or sent, but also while it’s being used.
Three Flavors of Homomorphic Encryption & What They Can Do
There are a few different versions of homomorphic encryption, depending on what kind of math you want to do:
- Partially Homomorphic Encryption (PHE) handles one type of operation, either adding or multiplying, but not both. It’s useful for simple tasks like tallying up encrypted values.
- Somewhat Homomorphic Encryption (SHE) can do both addition and multiplication, but only so many times before the math gets too messy to work with.
- Fully Homomorphic Encryption (FHE) is the full package. You can do any number of operations in any order, which makes it possible to run complex programs, like AI models, entirely on encrypted data.
FHE is the most powerful version, and while it’s still slower than working with unencrypted data, it’s getting faster and more practical every year.
What Makes Homomorphic Encryption Different From the Old Ways
There are a few major points that really set homomorphic encryption apart from its predecessor.
What’s Still Slowing It Down & Who’s Fixing It
Homomorphic encryption has a lot of potential, but it’s still not easy to use in practice. The biggest issue right now is performance. Doing math on encrypted data takes way more time and power than doing the same thing on regular data. Sometimes a thousand times more, sometimes even worse.
On top of that, encrypted files are huge, so you need a lot more memory and storage to work with them. That makes it tough to use HE in anything that needs speed or handles a lot of data.
To fix this, some companies are building hardware specifically designed for homomorphic encryption. Intel, Cornami, and Optalysys are all trying different approaches, including things like optical chips and stream-based designs that move away from traditional CPUs. The idea is to make encrypted computation fast enough that you don’t have to think twice about using it.
Meanwhile, groups like NIST and ISO are working on setting standards. Things like which parameters are safe to use and how to measure performance across different tools. That’s important because until there’s a shared framework, it’s hard for developers to know what “good” looks like. These efforts are still ongoing, but they’re moving in the right direction.
Fast-Start Developer Playbook
If you’re a developer new to homomorphic encryption, the best approach is to start small.
Pick a narrow use case
Something simple, like calculating the total of encrypted survey responses or running a basic filter on encrypted data. This helps you understand how the encryption and computation work without getting overwhelmed.
Choose a library that fits your needs
Prototype your idea
Once you have a basic setup, prototype your idea and run some benchmarks. Measure how long operations take, how large the encrypted data becomes, and how noise builds up over time. Then, tweak your implementation to improve performance and reduce memory use.Plan key management and compliance
Don’t forget about key management. Make sure you have a plan for how public, private, and evaluation keys are created, stored, and used. If you’re dealing with user data, make sure your approach aligns with relevant privacy regulations.Deploy to production or use FHE-as-a-Service
When you’re ready, you can move from testing into production. Some developers integrate homomorphic encryption directly into their apps, while others use cloud platforms that offer FHE-as-a-Service to handle the heavy lifting.
The Bottom Line
Homomorphic encryption has been talked about for years, but now it’s actually starting to show up in real tools and systems. It’s still not the fastest thing out there, but it’s finally getting to the point where developers can use it without needing a PhD or a custom setup.
For AI teams working with sensitive data, it offers something we haven’t really had before: the ability to run computations without ever exposing the data itself, marking a big leap for AI data privacy.
It’s not perfect, but in 2025, it’s becoming a practical option instead of just a cool idea on paper.
FAQs
What does homomorphic encryption do?
What is a practical example of homomorphic encryption?
What is the difference between traditional encryption and homomorphic encryption?
Is RSA a homomorphic encryption?
References
- Partially homomorphic encryption schemes over finite fields (Eprint)
- Somewhat Homomorphic Encryption based on Random Codes (Eprint)
- Fully Homomorphic Encryption without Bootstrapping (Eprint)
- Microsoft SEAL is an easy-to-use and powerful homomorphic encryption library (GitHub)
- Open-Source Fully Homomorphic Encryption Library (OpenFHE)
- Resources on the CKKS Fully Homomorphic Encryption Schemeg (CKKS)
- Integer FHE with BFV scheme (Pyfhel ReadTheDocs)
