Cryptography is full of mind-bending ideas, but few are as seemingly impossible as homomorphic encryption. It’s a method that allows you to perform computations directly on encrypted data without ever decrypting it first. Think of it like a magical, transparent jewelry box. You can put your valuable jewels (your data) inside, lock it up, and hand it to a jeweler (a cloud server or AI model). The jeweler can still see the jewels through the box, manipulate them, and even add new ones, but they can never actually touch or steal them. When they hand the box back to you, you’re the only one with the key to open it and retrieve the newly arranged jewels. The jeweler did their job without ever having access to the valuables themselves.
Homomorphic encryption (HE) is a form of encryption that permits users to perform computations on its encrypted data without first decrypting it. This is a radical departure from traditional encryption, which requires data to be decrypted before it can be processed, creating a moment of vulnerability. With homomorphic encryption, the data remains secure and unreadable from the moment it’s encrypted to the moment the final result is decrypted by its owner. This ability to compute on encrypted data is the holy grail of data privacy, as it allows us to use powerful cloud services and AI models without ever exposing our sensitive information (IBM, 2024).
For artificial intelligence, this is a game-changer. It means a hospital could use a third-party AI to analyze patient X-rays for signs of disease without the AI service ever seeing the actual medical images. It means you could get personalized recommendations from an online service without that service ever knowing your browsing history. Homomorphic encryption provides the mathematical foundation for a future where we don’t have to choose between powerful AI and personal privacy.
From Pipe Dream to Practicality
The idea of computing on encrypted data isn’t new. Cryptographers have been chasing this dream since the 1970s, shortly after the invention of modern public-key cryptography. They recognized that if you could manipulate encrypted data, you could build incredibly secure systems. For decades, however, it remained a theoretical pipe dream. Researchers developed partially homomorphic encryption (PHE) schemes, which were a step in the right direction. These schemes allowed for one type of mathematical operation to be performed on encrypted data, but not both. For example, you could have a system that supported unlimited additions, or a system that supported unlimited multiplications, but not a system that could do both. This was useful for certain niche applications, but it wasn’t the all-purpose solution cryptographers were looking for (IEEE, N.D.).
The real breakthrough came in 2009 when a researcher named Craig Gentry, then a PhD student at Stanford, published his groundbreaking thesis. Gentry described the first plausible fully homomorphic encryption (FHE) scheme, a system that could handle both addition and multiplication on encrypted data. This was the cryptographic equivalent of breaking the sound barrier. It proved that it was mathematically possible to perform arbitrary computations on encrypted data, opening the door to a whole new world of secure computing (Gentry, 2009).
Gentry’s initial scheme was a monumental achievement, but it was also incredibly slow. Performing even a simple computation could take days. The problem was that every time you performed an operation on the encrypted data, a small amount of “noise” was added to the ciphertext. After a few operations, this noise would build up and overwhelm the original signal, making the data impossible to decrypt. Gentry’s brilliant insight was a process called bootstrapping, a way to “refresh” the ciphertext and reduce the noise, allowing for an unlimited number of computations. But this bootstrapping process was itself incredibly computationally expensive.
In the years since Gentry's breakthrough, researchers have been working tirelessly to make FHE more practical. A new generation of FHE schemes, like Brakerski-Fan-Vercauteren (BFV) and Cheon-Kim-Kim-Song (CKKS), have dramatically improved performance. These schemes are based on different mathematical foundations (learning with errors on rings, or LWE) and have been optimized for the kinds of computations that are common in machine learning, like vector and matrix operations. While FHE is still much slower than computing on unencrypted data, the performance has improved by many orders of magnitude, and it's finally becoming practical for real-world applications (Apple, 2024).
The different FHE schemes each have their own strengths and trade-offs. BFV, for example, is designed for exact integer arithmetic, making it ideal for applications like secure database queries or statistical analysis where precision is critical. CKKS, on the other hand, is optimized for approximate arithmetic on real and complex numbers, which is perfect for machine learning applications where a small amount of precision loss is acceptable in exchange for much better performance. The choice of scheme depends on the specific requirements of the application, and developers often have to carefully balance the need for accuracy against the computational cost.
Another key development has been the creation of open-source libraries and frameworks that make FHE more accessible. Microsoft's SEAL, IBM's HElayers, and the open-source Concrete ML library from Zama.ai are all examples of tools that abstract away much of the complexity of working with FHE. These frameworks provide high-level APIs that allow developers to work with encrypted data using familiar programming paradigms, without needing to understand all the underlying cryptographic details. This democratization of FHE technology is crucial for its widespread adoption.
The Dawn of Private AI
With the performance of FHE steadily improving, we’re beginning to see it being used to build a new generation of privacy-preserving AI systems. These systems can leverage the power of machine learning without ever compromising the confidentiality of the data they’re trained on.
One of the most exciting applications is in the area of secure cloud computing. Many organizations want to take advantage of the powerful machine learning services offered by cloud providers like Google, Amazon, and Microsoft, but they're hesitant to upload their sensitive data to a third-party server. FHE provides a solution. A company can encrypt its data, upload it to the cloud, and then use the cloud provider's AI services to perform computations on the encrypted data. The cloud provider never sees the raw data, and the company gets the benefit of the AI's insights without giving up control of its information. This is particularly important in regulated industries like healthcare and finance, where data privacy is not just a preference but a legal requirement (AHIMA, 2025).
In healthcare, for example, hospitals could use FHE to collaborate on training diagnostic AI models without sharing patient data. A hospital could encrypt its medical images and send them to a cloud-based AI training service. The service could use those encrypted images to improve its model, and then send the updated model back to the hospital. The cloud service never sees the actual patient data, and the hospital gets a better AI model. This kind of collaboration could lead to major breakthroughs in medical AI, as it allows researchers to train models on much larger and more diverse datasets than any single institution could provide on its own. Similarly, financial institutions could use FHE to detect fraud patterns across multiple banks without sharing sensitive transaction data, or pharmaceutical companies could collaborate on drug discovery research without exposing proprietary compound information.
We’re also seeing FHE being used to build privacy-preserving features directly into consumer products. Apple, for example, uses homomorphic encryption in its Enhanced Visual Search feature. When your iPhone detects a landmark in one of your photos, it can encrypt an embedding of that image and send it to Apple’s servers. The servers can then use FHE to compare your encrypted embedding to a database of known landmarks and return the name of the landmark to your phone. Apple’s servers learn which landmark is in your photo, but they never see the photo itself or the embedding that was derived from it. This allows Apple to provide a useful service without compromising the privacy of your personal photo library (Apple, 2024).
Another major area of research is privacy-preserving machine learning as a service (MLaaS). In this scenario, a company might have a proprietary AI model that it wants to offer to customers as a service. But what if the customers’ data is too sensitive to be shared with the model owner? With FHE, the customer can encrypt their data, send it to the MLaaS provider, and the provider can run their model on the encrypted data. The customer gets the model’s prediction, and the model owner’s intellectual property is protected because the customer never sees the model itself. It’s a win-win that allows for the commercialization of AI without sacrificing privacy.
Recent breakthroughs are pushing the boundaries of what's possible with FHE in AI. Researchers at NYU recently developed a framework called Orion that can automatically convert deep learning models written in PyTorch into efficient FHE programs. They demonstrated the first-ever high-resolution object detection using a complex model called YOLO-v1, proving that FHE can handle real-world AI workloads with hundreds of millions of parameters. This is a major step toward making FHE a mainstream tool for AI developers (NYU, 2025).
How FHE Actually Works with AI Models
To understand how homomorphic encryption works with AI, it helps to think about what happens when an AI model makes a prediction. Most machine learning is just a series of mathematical operations: multiplications, additions, and sometimes more complex functions like activations. When you feed an image into a neural network, for example, the network multiplies the pixel values by various weights, adds them up, and passes the results through activation functions. This process repeats through multiple layers until you get a final prediction.
With FHE, all of these operations can be performed on encrypted data. The client encrypts their input data (like an image or a piece of text) using their private key and sends the encrypted data to the server. The server then performs the model's computations directly on the encrypted data. Multiplications of encrypted values produce encrypted results. Additions of encrypted values produce encrypted results. The server never sees the actual input data, and it never sees the intermediate values as the data flows through the model. When the computation is complete, the server sends the encrypted result back to the client, who can decrypt it with their private key to get the final prediction.
The challenge is that not all operations are equally easy to perform homomorphically. Additions and multiplications can be done relatively efficiently, but more complex operations like comparisons or non-linear activation functions (like ReLU or sigmoid) are much harder. This is why many FHE-based AI systems use simplified models or approximate complex functions with polynomial approximations. Researchers are actively working on developing new techniques to make these operations faster and more accurate, including the use of specialized hardware accelerators designed specifically for FHE computations.
The Performance Tax and Other Hurdles
Despite the incredible progress, homomorphic encryption is not a magic bullet. The biggest challenge remains performance. Performing computations on encrypted data is still orders of magnitude slower than performing them on unencrypted data. This “performance tax” can be a major barrier to adoption, especially for real-time applications. While researchers have made huge strides in optimizing FHE schemes and developing specialized hardware to accelerate them, it’s still a significant consideration. The overhead comes from the complexity of the mathematical operations and the large size of the ciphertexts, which can be thousands of times larger than the original data.
Another major challenge is the noise that accumulates in the ciphertext with each operation. Managing this noise is a delicate balancing act. If you let it grow too large, the data becomes undecipherable. The bootstrapping process developed by Gentry can “reset” the noise, but it’s a very slow operation. Modern FHE schemes have found more efficient ways to manage noise, but it remains a fundamental constraint that developers have to work around. This often means that AI models have to be redesigned or simplified to work with FHE, which can impact their accuracy.
Finally, there's the challenge of programmability. Writing programs that work with homomorphic encryption is not as straightforward as writing standard code. Developers have to think about how to structure their computations to minimize the number of operations, especially multiplications, which are the most expensive. They also have to carefully manage the noise and other parameters of the encryption scheme. This has led to the development of specialized compilers and frameworks, like IBM's HElayers and NYU's Orion, that aim to abstract away some of this complexity and make FHE more accessible to developers who aren't cryptography experts (IBM, 2024).
There's also the issue of data size. Encrypted data is much larger than unencrypted data. A single encrypted number might take up kilobytes or even megabytes of space, compared to just a few bytes for the unencrypted version. This means that transmitting encrypted data over a network can be slow, and storing large encrypted datasets can be expensive. Researchers are working on compression techniques and more efficient encoding schemes to reduce this overhead, but it remains a practical concern for many applications.
The Future is Encrypted
Despite the challenges, the future of homomorphic encryption is incredibly bright. As the performance continues to improve and the tools become more mature, we can expect to see FHE become a standard part of the AI toolkit. Researchers are actively working on developing new FHE schemes that are faster and more efficient, as well as specialized hardware accelerators that can bring the performance of FHE closer to that of unencrypted computation.
We're also likely to see more hybrid approaches that combine homomorphic encryption with other privacy-preserving techniques like federated learning and secure multi-party computation. For example, you could use federated learning to train a model across multiple devices, and then use homomorphic encryption to securely aggregate the model updates. This would provide an even stronger level of privacy than either technique could offer on its own.
The development of quantum-resistant FHE schemes is another important area of research. Many current encryption schemes, including some FHE schemes, could potentially be broken by future quantum computers. Researchers are working on developing new FHE schemes based on mathematical problems that are believed to be resistant to quantum attacks. This is crucial for ensuring that homomorphic encryption remains secure in the long term, even as quantum computing technology advances. Apple's implementation of FHE, for example, already uses parameters that provide post-quantum 128-bit security, meaning they're designed to withstand attacks from both classical and future quantum computers (Apple, 2024).
Another exciting development is the emergence of FHE-as-a-service platforms. Companies like Duality Technologies and others are building cloud platforms that make it easy for organizations to deploy FHE-based applications without having to become experts in the underlying cryptography. These platforms provide pre-built solutions for common use cases like secure data analytics, privacy-preserving machine learning, and confidential computing. This is lowering the barrier to entry and making it possible for a much wider range of organizations to benefit from FHE technology.
Ultimately, homomorphic encryption represents a fundamental shift in how we think about data privacy. It moves us from a world where we have to trust our service providers not to misuse our data to a world where we don’t have to trust them at all. The data remains encrypted and secure at all times, and we can still get the benefits of powerful AI and cloud computing. It’s a future where privacy is not an afterthought, but a mathematical guarantee built into the very fabric of our digital world.
