Homomorphic encryption is a cryptographic technology that allows computations to be performed directly on encrypted data, enabling artificial intelligence and other analytics to process sensitive information without ever decrypting it. This approach fundamentally changes how data privacy is managed, particularly in fields like healthcare and finance, by protecting information even while it is actively in use by third-party systems.

What is Homomorphic Encryption?

Traditional encryption methods are effective at securing data when it is stored (at rest) or being sent from one place to another (in transit). However, to perform any meaningful computation, this data typically must be decrypted first, creating a point of vulnerability. According to Duality Technologies, this lack of a solution for securing "data-in-use" has historically hindered data collaboration and analysis, especially as privacy laws become more stringent.

Homomorphic encryption solves this problem. It preserves the mathematical structure of data during encryption, allowing operations performed on the encrypted version (the ciphertext) to correspond directly to operations on the original, unencrypted data (the plaintext). This capability, long considered a theoretical "holy grail" of cryptography, is now being used in production for specific privacy-preserving applications.

This technology has transformative potential for professionals in sensitive fields. Robert Coombs, founder and CEO of Baton Health, notes that by enabling secure data sharing, homomorphic encryption can lead to more comprehensive datasets for AI and machine learning models. This could improve the accuracy of predictions for outcomes like disease risk or postoperative recovery, all while maintaining patient confidentiality.

Conceptual Model: Privacy-Preserving AI Inference with HE

To understand how homomorphic encryption works in a practical AI scenario, consider a medical diagnostic tool that uses a patient's symptoms to predict a condition. The goal is for the AI model, hosted on a remote server, to analyze the patient's private health information without the server owner ever seeing the actual data. The process unfolds in five key steps:

  1. User-Side Encryption: The process begins on the user's personal device. The user inputs their sensitive data, such as symptoms, into an application. This application uses a homomorphic encryption key to convert the plaintext data into ciphertext before it is ever transmitted.
  2. Transmission of Encrypted Data: The encrypted symptom data is sent over the network to the remote server where the diagnostic AI model is hosted. At this stage, the server receives only the scrambled ciphertext, not the original, readable health information.
  3. Computation on Encrypted Data: The AI model on the server performs its analysis—a process known as inference—directly on the encrypted data. Because the encryption is homomorphic, the mathematical operations of the AI model work on the ciphertext just as they would on the plaintext, even though the server cannot read the data.
  4. Return of Encrypted Result: After the AI model completes its computation, it produces a result, such as a diagnosis or risk score. This result remains in an encrypted format and is sent back to the user's device. The server never sees the final plaintext outcome.
  5. User-Side Decryption: Once the encrypted result arrives back on the user's device, the application uses the user's private key to decrypt it. Only at this final stage is the result converted back into readable plaintext, which is then displayed to the user.

This entire workflow ensures that the sensitive patient data and the resulting diagnosis are never exposed to the third-party service provider, maintaining confidentiality from end to end.

The Primary Trade-off: Computational Overhead

While homomorphic encryption offers powerful privacy guarantees, its primary practical limitation is significant computational overhead. According to an analysis from Encryption Consulting, current fully homomorphic encryption (FHE) implementations can be at least a million times slower than the same computations performed on unencrypted plaintext. This performance cost has major implications for real-world applications.

The same source translates this overhead into a tangible delay: operations that might take only milliseconds to complete on plaintext data could require hundreds of seconds when performed using FHE. This makes the technology unsuitable for many real-time or latency-sensitive AI tasks. Because of this, homomorphic encryption is not yet a universal replacement for traditional data processing.

Despite these performance challenges, ongoing advancements in algorithms and hardware are making the technology more practical. It is currently being deployed in production for high-value, privacy-critical applications where the need for security outweighs the performance cost, particularly in sectors like healthcare, finance, and machine learning inference.

Machine Learning over Encrypted Data with Fully Homomorphic EncryptionTo visually explain how machine learning can be performed on encrypted data using Fully Homomorphic Encryption, complementing the textual explanation of the step-by-step process.Watch on YouTube

Assessing Homomorphic Encryption for Your AI Privacy Needs

For organizations handling highly sensitive information, homomorphic encryption presents a viable path toward leveraging advanced AI without compromising user privacy. Its ability to secure data while in use makes it particularly well-suited for applications in healthcare and finance, where regulatory compliance and confidentiality are paramount. Google, for instance, is actively developing this technology with its open-source HEIR compiler, a tool designed to make cryptographically-secure private AI inference more practical.

Organizations should evaluate homomorphic encryption for AI applications involving highly sensitive data, particularly in healthcare and finance, where privacy regulations are stringent and the benefits of secure computation outweigh the current computational overhead. The decision to adopt it requires balancing the powerful privacy it enables against the performance trade-offs.

A key action for interested parties is to monitor the performance benchmarks and integration capabilities of HE libraries and frameworks (e.g., Google's HEIR) as they mature, looking for improvements in speed and ease of deployment for specific AI tasks. As the technology evolves, the balance between security and performance will continue to shift, potentially unlocking a wider range of privacy-preserving AI applications.

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