FHE has the potential to become a default standard for privacy-preserving AI
Initially imagined in the late seventies, the concept of Fully Homomorphic Encryption (FHE) far predates the modern Generative AI tech we know today. However, this year, we’ve seen the two become completely entwined, a shift that has all been driven by privacy.
AI itself dates back to the mid-50s when the term artificial intelligence was first coined. Setting the stage for decades of research and innovation, it wasn’t until around 2022, however, that we started to see AI become a major, life-changing force.
It’s when diffusion models, such as OpenAI’s DALL·E 2 and Stability AI’s Stable Diffusion, revolutionized generative image creation and we saw the emergence of OpenAI’s ChatGPT bringing conversational generative AI into mainstream use.
From that point on, we’ve seen AI enable some remarkable advancements, transforming how businesses and researchers operate. Through powerful data analysis, simulations and predictive modeling, it’s unlocked levels of efficiency, innovation and personalization never seen before.
However, in opening this door - a door that requires vast amounts of often sensitive or personal data to stay open - we’ve also opened up a can of worms; worms that include data privacy risks, bias, security threats, ethical concerns, trust and transparency issues and regulatory complexities.
It’s left us asking the question: how do we ensure data is accessible for AI's use while safeguarding privacy and adhering to ethical standards?
And it’s this very dilemma that’s helped bring FHE - a cryptographic method that allows computations to be performed on encrypted data without ever decrypting it - to the forefront.
Thanks to its ability to enhance privacy without compromising analytical performance, we’ve seen a host of tech giants showcase FHE’s potential of late. Just this year, for example, Apple’s privacy-preserving technologies team made its much-discussed swift-homomorphic-encryption announcement.
Apple’s initiative, which involves a type of FHE scheme known as Brakerski-Fan-Vercauteren, also follows similar, much earlier initiatives by Microsoft and IBM, who released HE libraries SEAL and HElib, in 2015 and 2013 respectively. However, because of Apple’s ability to integrate complex technologies into mainstream tools and workflows and the fact its library is designed for Swift - a programming language that’s widely used - what they’ve done is make it far more accessible to the developer community and truly thrust FHE into the spotlight.
As well as seeing interest spike in the industry this year, there have also been several recent breakthroughs that have pushed FHE closer to large-scale deployment. These have included new cryptographic schemes like CKKS optimizations, which support approximate arithmetic and are more efficient for AI tasks.
Improved algorithms have also enhanced bootstrapping processes, drastically reducing the time needed to refresh ciphertexts, while libraries such as TenSEAL and Concrete have been further optimized, making it easier for developers to deploy FHE at scale.
Additionally, hardware acceleration through GPUs and FPGAs has reduced computational demands, while more developer-friendly APIs have made integration into existing workflows more seamless.
As of now, though, several challenges remain, including the high computational cost of encryption, decryption and homomorphic operations, which can still be orders of magnitude slower than plaintext computations.
Scalability issues can also arise when handling large datasets, as current FHE schemes can struggle with memory and processing requirements. Additionally, real-time processing latency remains a significant hurdle for applications like video streaming or live analytics.
However, improvements around computational efficiency and cost decreases are already underway. And as these continue, broader FHE adoption in sectors like health care, finance and government, where data privacy is critical, will likely follow.
We believe health care could be the breakthrough use case for FHE, where it enables secure, privacy-preserving data analysis across institutions. For example, hospitals could collaborate on patient data for research or AI training without ever exposing sensitive information. Such a high-impact application would demonstrate the practical value of FHE and could position it as a must-have technology in industries dealing with sensitive data.
Looking ahead, as the tech world adapts to support FHE more effectively, its integration into AI workflows could accelerate, solidifying its role as a vital part of the privacy-enhancing technology toolkit.
Public perception of data privacy will also play a vital role in FHE demand. As people become more aware of how their data is used and the potential for misuse, there is increasing pressure on organizations to adopt advanced privacy measures.
With this in mind, we truly believe FHE has the potential to become a default standard for privacy-preserving AI within the next five to 10 years.
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