Last updated on September 18th, 2026 at 04:48 pm
The majority of encryption is merely a lockbox -You lock your data, then send it on. When it needs to be used, it has to be unlocked, which is the dangerous bit – short lives make you vulnerable to attacks.
Homomorphic encryption turns that model on its head. With it, systems perform operations on encrypted data and return only the encrypted output. The data itself is never exposed during the process. Only the final result is decrypted.
That‘s a whole new level of upgrade. That’s a whole new paradigm for data security.
Table of Contents
Why the “Encrypt Everything” Approach Has a Hidden Flaw
Encryption, which the world is familiar with, protects data at rest and in transit. But when a cloud server, a model, or a third-party service needs to do something with your data, it has to decrypt it. You rely on that platform not to be hacked, compromised, or malicious.
That‘s a pretty big ‘ask,’ even more so when you’re trying to access someone else’s server.
This is the fundamental problem HE (homomorphic encryption) is built to solve. We don’t decrypt data to manipulate it; we operate directly on the ciphertext. The math behind it is rather abstruse, but the principle is simple: encrypt the input, perform the calculation, and decrypt only the output.
The server doing the work will never actually see your data.
A Brief History That Most Articles Skip
It Started as a Math Problem, Not a Product
This idea originated in 1978, when Rivest, Adleman, and Dertouzos asked: can we compute on encrypted data without decrypting it? At the time, it was only theoretical; no one had a practical solution.
Craig Gentry broke it in 2009. His PhD thesis at Stanford gave us the first fully homomorphic encryption scheme. It worked, but it was vastly slow – so slow that encrypting just one bit of computation took minutes on hardware from that time.
That was a breathtaking moment. The confirmation that, mathematically, everyone was feasible.
The Decade of Refinement
A decade later, we saw the emergence of 2 new BGSBSGs, as well as another: BGV and BFV in 2010 each specialized in different kinds of calculations, allowing users to choose what best suited the task at hand. CKKS, the first to support approximate arithmetic, was introduced in 2017.
Libraries such as Microsoft SEAL, OpenFHE, and HElib can implement these schemes in practice, moving them from academic papers into working software. I have been exploring SEAL’s documentation and example implementations, and it’s been surprisingly straightforward, given how deeply mathematical it is.
What ‘Computing on Encrypted Data Without Decryption’ Is Really Like
A Real-World Scenario Worth Unpacking
Suppose a hospital wants to deploy an AI model, hosted in the cloud, to scan patient records for potential diagnoses. Here’s the rub: patient data is protected, and as soon as it’s uploaded to a third-party cloud service (even through an encrypted tunnel), the hospital becomes liable once the data is decrypted for processing.
In this scheme, the encrypted hospital records transferred into the cloud will be encrypted using homomorphic encryption. The encrypted file will then proceed to the cloud. AI model will perform analysis on the ciphertext. The output, which also remains encrypted, will then be returned. Only the hospital can decrypt it.
The cloud provider could not access real patient data, and the model cannot access names, diseases, or errors.
This is not speculative; both IBM and Microsoft have implemented pilot projects in health care with this very architecture.
Financial Services Are Already Testing This
Banks face similar constraints. When running fraud detection models, transaction data is constantly being revealed to third-party systems. With HE, the bank can outsource neither the computation nor the data.
My limited impression from skimming a handful of fintech security whitepapers is that HE is edging out the field, taking place together with PETs, the broader set including differential privacy, enclaves, anonymization. Compared to the entire PETs landscape, HE belongs to the stronger (but more time-consuming) group.
The Three Types You’ll Actually See Referenced
Not all homomorphic encryption is the same. There are three main categories:
Partially Homomorphic Encryption (PHE) provides support for only one type of operation addition or multiplication and not both. RSA is technically partially homomorphic. It is faster, but limited in the computations you can run.
Somewhat Homomorphic Encryption (SHE) supports both addition and multiplication, but only up to a limited computation depth. Suitable for simpler ML models or simple analytics.
Supports unlimited operations of anything that is Fully Homomorphic Encryption (FHE). The strongest and most impressive use cases are also the most expensive in terms of computation.
Most practical applications are still experimenting with FHE, often using FHE optimizations or a hybrid of FHE with other techniques on top so that the policy can be implemented at scale.
Where It Actually Works and Where It Struggles
Strong Use Cases Right Now
- Healthcare analytics: elaborate on how you can analyze data in the cloud without revealing sensitive information to the cloud infrastructure
- Integrated financial modeling: Running a joint risk model across competing institutions without exchanging nonaggregated data.
- Genomics: analyzing genetic data for research without disclosing individual sequences.
- Private AI inference: Submitting queries to a model without the provider of the model seeing your input.
Third, this is by far the most intriguing in the AI epoch. If you’re using a proprietary AI system and want to hide your prompts or data, HE could, in theory, let you get results while completely hiding your query from the model provider.
Where It’s Not Ready Yet
The speed penalty is real. FHE operations can be 1,000x to 1,000,000x slower than the same operation on plaintext, depending on the scheme and hardware. For real-time systems, that’s a killer.
Storage overhead is yet another problem: encrypted data with HE is magnitudes greater than its plaintext counterpart. And the complicated deployment means even smart engineers need a couple of weeks to get it properly working.
I just found out that almost all production deployments out there use HE for offline/batch processing, not anything that needs real-time response. That‘s an honest constraint to know upfront.
How It Connects to Federated Learning and Multi-Party Computation
It does not travel alone. Useless by itself, HE fits into a family of privacy-preserving techniques, and understanding its place in that family matters.
Federated Learning and Multi-Party Computation are two approaches with similar end goals: performing computations on data across multiple remote, confidential sites without aggregating raw data. Federated learning lets local devices keep their data and exchange only model updates, while Multi-Party Computation protocols often let several parties compute a function simultaneously.
HE can also serve as a layer agnostic to either. For example, in federated learning, edge devices can encrypt model updates with HE so the server can combine them without revealing the gradients, even though gradients can sometimes leak information about the training data.
Combining methods provides stronger privacy guarantees than any single method. For example, Google and Universities like MIT have published papers on these hybrid architectures, and they are starting to emerge in more privacy-conscious AI research.
My Take After Going Through the Developer Experience
The tooling is improved, but is still relatively niche.
Microsoft SEAL is arguably the most accessible starting point. Supports BFV and CKKS schemes, C++ and .NET API‘s, and the documentation is (somewhat) readable. OpenFHE is aimed more at research but is more flexible. HElib, from IBM, is robust for ‘real’ production experimentation.
With SEAL’s sample code, I learned that simple tasks adding, encrypting, and decrypting a handful of integers can be picked up in an afternoon. What’s harder is grasping the noise budget, which tells you how many tasks you can run before decryptability fails. This is where the real knowledge of FHE is.
For the developer crowd: that’s not something you become familiar with by doing a weekend side project. Yet it’s no longer only in the hands of those PhDs in cryptography.
What the Cloud Providers Are Doing
Teams are working on HE acceleration (software and hardware) across AWS, Google Cloud, and Microsoft Azure. AWS has published work on hardware-accelerated FHE. Intel has been integrating HE acceleration into its processor roadmap.
The message is loud and clear: major cloud players view this as an inevitable infrastructure need, not a niche research topic.
Two Things Most Articles Don’t Tell You
Let’s clarify two points initially. First: HE is not a panacea. Even if all the computations are encrypted, an attacker monitoring the computation process, by noting which operations are being executed, their frequency, and their order, might have some knowledge of the data that was processed: it’s an ‘access patterns attack’, and that’s a true disadvantage of HE.
Second: Fully Homomorphic Encryption is not a security reference; it refers to the operations the scheme can perform. The implementation, scheme choice, and parameter selection all affect security and performance. Do not treat FHE as a cure.
Honest Verdict: Who Should Be Paying Attention to This
If this is healthcare IT, finance, legal technology, or any area where external systems need to access and interpret sensitive data, find out about this now, not second… In the future, the squeeze on data privacy (GDPR, HIPAA, future AI regulations) will only tighten, and HE offers a technical solution that fits that direction.
As a developer or engineer working in AIEF, HE is clearly relevant to the paradigm we are moving toward: privacy-preserving machine learning. If any of your algorithms run inference over sensitive inputs, or collaborate across organizations, your toolkit is becoming relevant.
For those working in highly technical fields, this is the kind of mandatory upgrade you won’t notice as a user, but that will greatly affect how reliable the system you access is. Can you search an encrypted database without decrypting it? Can an AI infer anything about your data without ever seeing it? That is what this will enable at scale.
It’s not quite there (yet). Though not as far off as most people think.
I’m a technology writer passionate about AI and digital marketing. I create engaging and useful content that bridges the gap between complex technology concepts and digital technologies. My writing makes the process easy and engaging. I encourage participation I continue to research innovation and technology. Let’s connect and talk technology!
