A Comprehensive Guide to Homomorphic Encryption

Traditional encryption protects data at rest and in transit—but not while it’s actively being used. This leaves a critical gap in cybersecurity, especially for organizations relying on cloud services and third-party processing platforms.

Homomorphic encryption closes this gap by allowing encrypted data to be analyzed and processed without ever being decrypted. This ensures continuous protection, even during complex computations.

A recent survey published by Security Magazine revealed that 90% of IT professionals believe encryption positively impacts network, data, and overall security, with more than half reporting a significantly positive effect. Still, nearly 33% identified the lack of encryption as the top cause of sensitive data loss—highlighting the urgent need for stronger encryption strategies.

In this blog, we’ll explore what homomorphic encryption is, how it works, its key types, use cases, benefits, and challenges. We’ll also examine how it strengthens data protection across sectors like healthcare, finance, and the cloud.

What Is Homomorphic Encryption?

Homomorphic encryption is a method that allows data to remain encrypted while it’s being processed. Instead of decrypting data to perform calculations, this technique enables computations directly on the encrypted form (ciphertext). When the result is decrypted, it matches what you’d get if the operations were done on the original data.

This means sensitive information stays protected throughout its entire lifecycle—including during analysis. For organizations handling confidential data, homomorphic encryption is powerful because it eliminates the need to expose sensitive information during processing. Whether it's analyzing financial records, personal health data, or employee behavior patterns, the data stays encrypted throughout—reducing the risk of leaks or insider threats.

To better understand how encryption fits into a broader cybersecurity strategy, read Keepnet’s article: What Is Data Encryption?

Why Use Homomorphic Encryption?

As organizations increasingly rely on cloud services and remote collaboration, they often outsource data processing—widening the risk of unauthorized access. Traditional encryption protects data when stored or transmitted but leaves it exposed during processing.

Homomorphic encryption addresses this critical gap by keeping data encrypted throughout its entire lifecycle, including during computation. This makes it ideal for environments where data privacy and security are essential, such as finance, healthcare, and government.

When paired with layered defenses like Security Awareness Training, it helps build a comprehensive security strategy that reduces both technical vulnerabilities and human error.

How Does Homomorphic Encryption Work?

Homomorphic encryption lets you do calculations on encrypted data—without ever needing to unlock it. Here’s how it works in a simple step-by-step flow:

• First, the data is encrypted, turning it into unreadable code.
• Next, you perform calculations (like adding or multiplying) directly on the encrypted data.
• Even though the data stays encrypted, the calculations still work correctly.
• The result of those calculations is also encrypted.
• Finally, when you decrypt the result, you get the same answer you would have if the data had never been encrypted at all.

This means your data stays safe and hidden the entire time—even when it's being processed. That’s why it’s so useful in things like homomorphic encryption in cloud computing or training private models in homomorphic encryption machine learning.

Categories of Homomorphic Encryption

Homomorphic encryption comes in different types, based on how many and which kinds of operations it supports. Each category balances between performance and flexibility, depending on the level of encryption needed. Understanding these types helps organizations choose the right solution for their specific security and processing needs.

Partially Homomorphic Encryption

This type supports only one type of operation—either addition or multiplication, but not both. It’s fast and efficient, making it suitable for simple tasks where only one type of calculation is needed, such as secure email filtering or Email Threat Simulation.

Somewhat Homomorphic Encryption

This version allows both addition and multiplication, but only a limited number of times. As more operations are performed, the encrypted data becomes harder to work with due to added noise. It's useful for tasks that need basic processing, and can work alongside tools like Incident Responder for encrypted threat analysis.

Fully Homomorphic Encryption

Fully homomorphic encryption supports unlimited addition and multiplication operations on encrypted data. This allows for full-scale data analysis and model training without exposing the data, making it ideal for secure cloud analytics, AI development, and homomorphic encryption machine learning. It’s the most powerful—but also the most complex and resource-intensive—form of homomorphic encryption.

Benefits of Homomorphic Encryption

Homomorphic encryption provides a powerful way to protect data even while it’s being processed. This makes it a strong solution for organizations that prioritize security, privacy, and compliance.

Here are some of the key benefits it offers:

• End-to-End Data Security: Keeps data encrypted during storage, transfer, and processing—minimizing the risk of exposure. To learn how this approach strengthens data privacy, read Keepnet’s article on What Is End-to-End Encryption?
• Secure Cloud Computing: Enables safe data analysis in the cloud without revealing sensitive information to providers.
• Regulatory Alignment: Supports compliance with data protection regulations like GDPR and HIPAA by preventing unauthorized access.
• Confidential Collaboration: Allows multiple parties to work on encrypted data without sharing the raw information.
• Trust in Outsourcing: Builds confidence when outsourcing data operations, knowing the content remains hidden at all times.

Homomorphic Encryption Use Cases

Homomorphic encryption is highly valuable in environments where data privacy must be maintained—even during processing. Its ability to compute on encrypted data makes it ideal for secure, real-time collaboration and analytics across industries.

• Finance: Banks can analyze encrypted transaction data to detect fraud or assess risk without exposing customer information.
• Healthcare: Medical institutions can process patient data for research or diagnosis while keeping it confidential to meet privacy regulations.
• Government: Agencies can perform secure data sharing and analysis across departments without disclosing sensitive national or citizen information.
• Cloud Services: Organizations can safely outsource data analytics to cloud providers using homomorphic encryption in cloud computing, reducing trust concerns.
• Telecommunications: Carriers can study usage patterns or performance metrics without compromising subscriber privacy.

These use cases show how homomorphic encryption use cases support both security and operational efficiency across sensitive workflows.

Challenges of Implementing Homomorphic Encryption

While homomorphic encryption offers strong security benefits, it also comes with practical challenges that organizations must consider before adoption.

• High Computational Overhead: Processing encrypted data is significantly slower than working with plaintext, which can impact system performance.
• Complex Implementation: Developing and integrating a homomorphic encryption algorithm requires advanced cryptographic expertise and careful system design.
• Scalability Issues: Applying homomorphic encryption to large datasets or high-volume processes can be resource-intensive and hard to scale.
• Key Management: Securely storing and managing encryption keys is critical; any compromise could undermine the entire system.
• Limited Tool Support: Compared to traditional encryption, there are fewer mature tools and frameworks available, making deployment more difficult.

These challenges highlight the importance of planning, testing, and using reliable platforms when implementing homomorphic encryption in real-world environments.

Performance Considerations in Homomorphic Encryption

One of the biggest limitations of homomorphic encryption is its performance. Processing encrypted data requires significantly more computational power and time compared to plaintext operations.

Factors such as the type of encryption scheme used, the number of operations performed, and the size of the dataset all impact speed and efficiency. While techniques like batching and bootstrapping help improve performance, they still add complexity.

Organizations should carefully evaluate their use cases to ensure the security benefits outweigh the performance trade-offs, especially in high-volume or real-time environments.

Homomorphic Encryption Algorithms and Standards

Several homomorphic encryption algorithms have been developed to support secure data processing with varying levels of performance and complexity. The most commonly used schemes include:

• BFV and BGV: Designed for exact computations, suitable for scenarios like secure voting or encrypted financial calculations.
• CKKS: Supports approximate arithmetic, making it ideal for applications like encrypted analytics and statistical modeling.

To ensure safe and consistent implementation, global standards are being developed by organizations like ISO/IEC and industry groups such as HomomorphicEncryption.org. These standards help guide the use of homomorphic encryption algorithms in real-world systems, promoting interoperability and best practices.

Comparison with Other Encryption Techniques

Traditional encryption methods, like AES or RSA, protect data when it's stored or transmitted—but not while it's being used. To process data, it must be decrypted, which creates a security gap.

The table below highlights the key differences between the two approaches.

Table 1: Key Differences Between Traditional and Homomorphic Encryption

Homomorphic encryption closes this gap by keeping data encrypted during computation, offering stronger protection in cloud and outsourced environments. While it improves security, it also demands more processing power and is slower than traditional encryption—making it ideal for privacy-critical, not performance-critical, tasks.

Security Concerns and Limitations of Homomorphic Encryption

While homomorphic encryption offers strong privacy and data protection, it’s not without limitations. One key concern is performance—encrypted computations are slower and require more processing power, which can impact usability in real-time systems.

Another challenge is implementation complexity. Developing and managing a secure homomorphic encryption system demands advanced cryptographic knowledge and careful key management. If keys are mishandled or compromised, the entire system’s security can be at risk.

Additionally, noise accumulation during encrypted computations can affect accuracy and reliability, especially in deep or repeated operations.

To build a layered defense that addresses both human and technical risks, consider integrating encryption with tools like Keepnet's Incident Responder—which helps detect, investigate, and mitigate threats in real-time, even in encrypted environments.