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The interest in fully homomorphic encryption (FHE) is growing as companies search for stronger data privacy solutions in a world that is increasingly regulated.
FHE allows data to be processed without ever being decrypted, which is a breakthrough that could completely transform industries where data security is of utmost importance.
To delve deeper into this technology, I intend to address some of the most commonly asked questions about FHE, its current capabilities, and its potential to redefine secure data processing in the future.
What inspired the development of FHE
Homomorphic encryption was developed gradually over several decades, starting with the accidental discovery of partially homomorphic systems and then progressing more purposefully until its full potential was realized in 2009 and the following decade.
The implications of these developments were extraordinary – we could now send data to the cloud, an AI engine, or another third party for processing without worrying about privacy breaches.
However, the computational power required to implement FHE was significantly greater than computing ‘in the clear,’ making it difficult to achieve widespread adoption and limiting FHE to an academic pursuit.
Now, however, the interest in and advancement of FHE is being driven by new factors.
Companies must navigate a complex legal framework that includes GDPR in Europe, CCPA in California, and various regulations in at least 14 other US states.
Despite this, the demand for third-party data continues to grow, as companies rely on the ability to analyze new data to solve complex problems such as detecting financial fraud or researching medical treatments.
At the same time, other privacy-assuring alternatives to FHE face significant challenges.
Confidential computing methods like trusted execution environments (TEEs) have repeatedly demonstrated vulnerability to side-channel attacks and direct breaches, putting the companies that rely on them at risk.
Other privacy-assuring approaches, such as secure multi-party computation, typically require networks of computers to be online together throughout computations, which necessitates complex network configurations and leaves them vulnerable to failure if any participating machines or network links go down.
In contrast, FHE offers cryptographically sound privacy guarantees, requires no complex network configurations, and relies solely on the reliability of a single compute server.
The combination of strong privacy guarantees and simplicity of deployment makes FHE a compelling solution for ensuring practical and secure privacy in fields like finance and healthcare, where privacy is crucial.
With FHE, companies can perform computations on encrypted data, ensuring that data remains protected during storage, transit, and processing.
We are now on the verge of a new era in data privacy. Within a generation, the concept of sharing or outsourcing computation on unencrypted data will no longer exist.
Can you explain the concept of computing on encrypted data and why it is considered a breakthrough in data privacy
In the past, we have encrypted data at rest, such as on disk drives, and during transit over networks.
However, in order to process data, we needed to decrypt it because existing encryption mechanisms did not allow for computation.
Decrypting the data also made it visible to anyone performing the computation, which required the data owner to trust those conducting the computation.
New encryption schemes, like those used in FHE, not only prevent the data from being revealed but also allow for computation on the encrypted data.
As a result, data owners no longer need to trust those performing computations to keep the data private.
This breakthrough, known as “zero trust, full computation,” fundamentally changes the relationship between data owners and data processors, enabling the outsourcing of computation without the risk of data compromise.
What are the main challenges associated with implementing FHE in real-world applications
I see three main challenges:
1. The computational complexity of FHE poses a performance challenge.
FHE computations are significantly slower than unencrypted computations, often by several orders of magnitude, making it difficult to achieve practical performance levels.
This slowdown is due to the additional work required by CPUs and GPUs to manage the complex data representations used in FHE.
2. The data expansion typically seen in FHE encryptions poses a storage and network bandwidth challenge.
Homomorphically encrypted data is much larger than unencrypted data, requiring significantly more storage space.
Current research ideas, like hybrid FHE, are not yet developed enough to address this challenge.
3. The complex algorithms required for FHE computations pose a usability challenge.
Programming in FHE, even with the availability of great FHE libraries, is difficult due to the numerous parameters that must be correctly chosen and the auxiliary operations needed to manage FHE computations, which cannot be automatically handled by existing programming tools.
How does the process of encrypting data for FHE work, and what role does homomorphism play in this process
To fully explain this process, we would need to discuss topics like Gaussian noise sampling, polynomial representations of data, residue number systems, the math problem of “learning with errors,” public key encryptions, prime modular arithmetic, and high-dimensional vector spaces. However, these topics are not suitable for general discussion.
In summary, in FHE, we move data from the regular number line to an alternative space.
What is important is that the movement of the data itself is the encryption, which is difficult to undo without a specific key.
Another important aspect is that the alternative space must be homomorphic (meaning “same shape”) to the regular number line with respect to multiplication and addition. This allows us to perform additions and multiplications on the data in the alternative space, knowing that when we move the data back through decryption, these operations will have the intended effect on the data.
What types of programs or computations are best suited for FHE, and are there any limitations on the types of data or operations that can be performed
The security provided by FHE is partly based on adding a small amount of “noise” to the data during the encryption process.
One limitation of FHE is that when you add or multiply the data, the noise grows, as expected.
After a certain number of operations on a data item, the noise can become too large, making decryption impossible.
To address this problem, FHE uses an expensive process to remove noise without revealing the data, allowing computation to continue.
This noise removal process must be performed every few operations to keep the data fresh, but it is the most time-consuming operation in FHE, accounting for up to 95% of computation time.
With this in mind, the best-suited computations for FHE are those that do not require many operations in sequence on the data, minimizing the need for noise removal.
Examples of such computations include linear algebra and private information queries.
Expanding on these ideas, statistical computations like regressions, certain types of image processing, and even relatively simple neural networks can be suitable for FHE-assured privacy.
Optimization and careful selection of use cases are crucial for maximizing the benefits of FHE.
What are some practical applications that would benefit most from FHE
FHE unlocks entirely new applications across industries that would be impossible without mathematically guaranteed privacy.
While some of these applications are still challenging to implement on a large scale with FHE today, they are all promising targets for hardware-accelerated FHE in the near future.
Healthcare statistics
FHE enables the analysis of health records on a large scale while maintaining patient privacy.
Clinicians and insurance providers can analyze data on patient satisfaction, hospital readmission rates, and other factors across their patient populations.
This comprehensive analysis helps identify more effective treatments and personalized care plans, ultimately improving overall quality of life.
Finance
FHE allows for secure sharing of financial transaction data across institutions and borders, enabling banks to identify fraudulent accounts and transactions regardless of their origin.
This enhanced ability to detect and prevent fraud strengthens the integrity of the financial system.
Machine learning
FHE enables machine learning models to analyze sensitive data without exposing the data itself.
For example, image recognition can identify security threats or legal violations without intrusive surveillance, and medical scans can be analyzed without risking the exposure of patient data.
Market intelligence
FHE enables manufacturers to share inventory, sales, distribution data, and more with analysts, data brokers, and even competitors.
This collaboration enhances the ability to predict and respond to market changes and effectively manage supply chains.
Data brokers can also perform computations on private data, such as GPS locations, to uncover valuable insights at a population level without compromising individual privacy.
Cross-organizational coordination
FHE enables secure and private data sharing between different jurisdictions.
What advancements have been made to improve the performance of FHE, and how does the current performance compare to traditional unencrypted computations
Advancements in FHE have focused on optimizing algorithms, developing specialized hardware for faster processing, and utilizing parallel processing to some extent.
Despite these improvements, FHE remains slower than traditional unencrypted computations.
In well-suited applications, FHE can still be significantly slower, but ongoing efforts are aimed at closing this performance gap.
Recent progress, including the acceleration of FHE computation with dedicated hardware, has made it viable for applications in sectors such as finance, AI, machine learning, insurance, and healthcare, where data security and privacy are paramount.
Feedback from users indicates that the current speed of FHE is sufficient for many new and previously challenging use cases, enabling companies to analyze data securely without revealing its contents.
Furthermore, proof-of-concept work in industries like machine learning and fraud detection has demonstrated that current FHE performance is adequate for practical use, with ongoing refinements expected to further improve it.
What are the future goals for FHE technology, and what developments can we expect in the next few years
Many companies and investors have recognized the potential of FHE to revolutionize the data economy. There has been at least $200 million worth of venture investment in FHE hardware acceleration alone, and the US Government has made substantial investments through programs like DPRIVE by DARPA.
These investments are driving towards the future goal of achieving performance parity between FHE and traditional unencrypted computations, making FHE practical for a wider range of applications.
In the next few years, developments are expected to focus on further optimizing algorithms and hardware to reduce computational overhead, developing user-friendly programming interfaces and tools to simplify FHE implementation, and expanding the range of practical applications through proof-of-concept projects and real-world deployments to demonstrate the value of FHE in various industries.
Dr. David Archer, the CTO of Niobium, is a leading expert in “zero trust computing” solutions. He is one of the world’s foremost experts in advanced cryptography, and a pioneer researcher and principal scientist in secure multiparty computation and homomorphic encryption.
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