What Is Homomorphic Encryption and Why It Matters for Artificial Intelligence

As artificial intelligence (AI) becomes increasingly integrated into our daily lives—from healthcare and finance to smart homes—the question of how to protect data in these systems is becoming more urgent. AI works with vast datasets, often containing sensitive personal information, making data security not just a technical challenge but also a legal and ethical imperative. In this context, homomorphic encryption emerges as a revolutionary technology that allows data processing without ever decrypting it.

This article explores what homomorphic encryption actually is, how it works, what types exist, and most importantly: why it could become a cornerstone of future AI systems. We’ll walk through the topic with clarity, from historical background to practical examples, making it accessible to both beginners and advanced users.

Historical context and evolution

The idea of homomorphic encryption isn’t new, but for decades it remained purely theoretical. As early as the 1970s, researchers imagined a way to perform calculations on encrypted data—without revealing the content of that data. A major breakthrough came in 2009, when Stanford PhD student Craig Gentry introduced the first fully homomorphic encryption (FHE) scheme.

Since then, this milestone has fueled extensive cryptographic research. Major tech players like Microsoft, IBM, and Google have been investing in practical applications, while researchers continue developing more efficient algorithms. The goal is to enable cloud-based, distributed, or AI-powered processing without the risk of data leaks.

What is homomorphic encryption?

Homomorphic encryption (HE) is a specialized cryptographic method that allows mathematical operations to be performed directly on encrypted data. The results of these operations, once decrypted, match the results you would get if you’d applied them to the original plaintext data.

Simple example

Imagine you have two numbers: 5 and 3. After encrypting them, a system performs an addition operation without knowing the values themselves. With homomorphic encryption, it still returns the correct result—8—after decryption. This capability allows data to remain encrypted throughout processing, so the computing entity never accesses the actual content.

Types of homomorphic encryption

Partially Homomorphic Encryption (PHE)

Supports only one type of operation (e.g., only addition or only multiplication). Examples include RSA (multiplication) and Paillier (addition).

Somewhat Homomorphic Encryption (SHE)

Supports multiple operations but in a limited number. As more operations are performed, the encrypted data becomes noisier, potentially compromising decryption accuracy.

Fully Homomorphic Encryption (FHE)

The “holy grail”: allows unlimited operations of any type on encrypted data. It’s theoretically perfect but currently computationally intensive.

How does it work in practice?

1. Encryption – Raw data is encrypted using the user’s key.
2. Computation – Mathematical operations are performed on the encrypted data (e.g., in AI systems: decision trees, statistical analysis).
3. Decryption – The output matches what would be produced using the plaintext data.

This approach is especially beneficial for cloud environments where data processing happens on third-party servers—the service provider never sees the unencrypted content.

Why is this critical for AI?

AI systems rely on huge volumes of data, often including:

• Health records
• Financial transactions
• Behavioral analytics

With homomorphic encryption, this data can be used for training or decision-making while remaining private:

• Privacy is preserved
• Easier compliance with GDPR and similar regulations
• Minimized risk of misuse
• Greater user trust in AI systems

Real-world use cases

Healthcare

AI systems can analyze thousands of CT scans or lab results that remain encrypted throughout the process. Doctors benefit from AI-generated insights without compromising patient confidentiality.

Finance

Risk assessments, credit scoring, and fraud detection can be conducted without exposing sensitive customer information.

Government

Data collection for census, urban planning, or social analytics can be performed securely without exposing citizens’ private data.

Benefits and challenges

Benefits

• Maximum data protection: even processors don’t access the actual data.
• Ethical AI: aligns with data protection principles.
• Flexibility: applicable in sectors from healthcare to cybersecurity.

Challenges

• Performance: FHE is still significantly slower than traditional processing.
• Infrastructure: high demands on CPU and memory.
• Complexity: implementation is difficult and expertise is rare.

Existing tools and libraries

• Microsoft SEAL – C++/.NET library that’s developer-friendly.
• IBM HELib – Advanced open-source FHE solution.
• PALISADE – Used in both academia and industry.
• TenSEAL – Designed for Python and PyTorch in machine learning contexts.

Where is the technology headed?

• GPU acceleration: dramatic speedups using graphics processors.
• Federated learning integration: AI models trained across devices without sharing raw data.
• Edge AI: local devices processing encrypted data without offloading.
• International standardization: unified HE protocols on the horizon.

Homomorphic encryption isn’t just an exciting innovation—it may be one of the most essential technologies for implementing secure and ethical AI. While current technical challenges remain—especially in terms of speed and complexity—practical applications are on the rise.

Anyone involved in AI development today should understand this concept. With growing regulatory requirements and user expectations, homomorphic encryption could well become the backbone of privacy-preserving AI.