What Is Homomorphic Encryption?

Key Takeaways:

• Homomorphic encryption allows computations to be performed directly on encrypted data. The data stays protected throughout the process, eliminating the need to decrypt it for analysis or processing.
• Organizations can outsource data processing, such as to cloud providers or analytics firms, without sharing raw or sensitive information. This reduces trust barriers and minimizes the risk of data exposure.
• Because the data is never exposed during processing, the risk of tampering or misuse is drastically reduced. This builds stronger auditability and trust in digital services.

 

Encryption is a key part of how we protect digital information. By converting readable data into ciphertext, it keeps sensitive content hidden from anyone without the proper decryption key. This helps secure everything from personal messages to financial records.

But standard encryption comes with a tradeoff. To process or analyze encrypted data, it typically must be decrypted first. That step can expose private information and undermine the very protection encryption is meant to provide.

This challenge is becoming more pressing as organizations increasingly rely on third-party platforms to store and process data. When privacy and compliance are on the line, even brief exposure of sensitive information can create real risks. There’s a new approach that helps keep data protected, even while it’s being used. In the next section, we’ll take a closer look at what homomorphic encryption is and how it works.

What Is Homomorphic Encryption?

Homomorphic encryption is a form of cryptography that allows computations to be performed directly on encrypted data without first decrypting it. When the final result is decrypted, it matches the outcome that would have been produced if the operations had been conducted on the original, unencrypted data. This capability enables the secure processing and analysis of sensitive information while keeping it confidential throughout the workflow.

The term “homomorphic” comes from the Greek for “same structure,” reflecting the encryption’s ability to preserve the mathematical structure of the data. This allows encrypted inputs to be manipulated in meaningful ways without exposing their contents.

By maintaining privacy during computation, homomorphic encryption allows third parties—such as cloud service providers or research institutions—to process data securely without ever accessing the raw information. This approach is especially valuable in privacy-sensitive sectors like healthcare and cloud computing, where regulatory and ethical concerns demand strong safeguards for personal and proprietary data.

Although still developing in terms of scalability and speed, homomorphic encryption represents a major step forward in balancing data utility with privacy protection.

The Three Types of Homomorphic Encryption

Homomorphic encryption is generally categorized into three types: Partially Homomorphic Encryption (PHE), Somewhat Homomorphic Encryption (SHE), and Fully Homomorphic Encryption (FHE). Each type varies in the kinds and number of operations it supports on encrypted data.

1. Partially Homomorphic Encryption (PHE)

2. Somewhat Homomorphic Encryption (SHE)

SHE supports both addition and multiplication but only for a limited number of operations. As more operations are performed, the noise in the ciphertext increases, eventually making the data unreadable if not managed. SHE is more versatile than PHE and is often used in scenarios where only a few encrypted operations are required, such as basic statistical calculations or controlled machine learning tasks.

3. Fully Homomorphic Encryption (FHE)

FHE is the most comprehensive type, allowing an unlimited number of both addition and multiplication operations on encrypted data. This makes it possible to perform complex computations—like running full algorithms or models—without ever decrypting the data. While FHE is still computationally demanding and not yet practical for all real-time use cases, it is advancing rapidly. The Brakerski-Fan-Vercauteren (BFV) scheme is one notable implementation that supports secure, large-scale data processing.

The table below summarizes the main differences between the three types:

 

Types of Homomorphic Encryption	Operations	Number of Operations
Partially Homomorphic Encryption (PHE)	One (addition or multiplication)	Unlimited
Somewhat Homomorphic Encryption (SHE)	Two (addition and multiplication)	Limited
Fully Homomorphic Encryption (FHE)	Two (addition and multiplication)	Unlimited

Why Does Homomorphic Encryption Matter?

Standard encryption protects information in storage or transit but often breaks down during use. Decryption before processing creates a moment of exposure that can put privacy and security at risk. Homomorphic encryption solves this by allowing data to be used while still protected, enabling more secure and compliant operations.

Here are the key benefits that make this technology increasingly valuable:

1. Private Computation Without Exposure

Organizations can analyze and process encrypted data without ever accessing the plaintext. This allows sensitive workloads to be handled by third-party services without compromising user privacy.

2. End-to-End Confidentiality

With encryption maintained throughout the entire lifecycle of the data, risks from breaches, leaks, or unauthorized internal access are significantly reduced. This is especially important in fields where compliance with privacy regulations is mandatory.

3. Streamlined Data Workflows

Because the need to decrypt and re-encrypt is removed, data operations can become more efficient. This helps scale privacy-preserving analytics across complex infrastructures like multi-cloud systems or distributed databases.

4. Trustworthy Results and Reduced Risk

By eliminating exposure during computation, homomorphic encryption reduces the chance of data manipulation or tampering. It supports cleaner audit trails and helps organizations prove the integrity of the output—even when processed by external entities.

Applications of Homomorphic Encryption

Homomorphic encryption is gaining traction across industries where secure data handling is critical for analysis, collaboration, and regulatory compliance. It is especially valuable in sectors that work with sensitive or regulated information and aim to reduce exposure risk. Below are some of the key areas where this technology is being actively implemented:

1. Secure Cloud Computing

Many organizations rely on cloud infrastructure for data storage and computation, but concerns over privacy often limit the type of workloads that can be outsourced. Homomorphic encryption allows sensitive data to remain protected even during processing. IBM, in partnership with Banco Bradesco, conducted a pilot using fully homomorphic encryption to perform financial modeling on encrypted customer data. This demonstrated that secure outsourcing is possible without sacrificing data privacy or regulatory compliance.

2. Privacy-Focused Data Analysis

Companies and research institutions need to analyze growing volumes of personal and financial data while complying with strict privacy regulations. Microsoft’s SEAL library offers an open-source toolkit for privacy-preserving analytics. It enables users to detect fraud, conduct medical research, and perform compliance checks while ensuring the underlying data remains encrypted throughout the process. This capability is increasingly relevant in healthcare, finance, and personalized marketing.

3. Secure Multi-Party Computation

In finance, healthcare, and government, multiple stakeholders often need to jointly analyze data without directly sharing proprietary or sensitive information. Homomorphic encryption supports this through secure multi-party computation, allowing results to be computed collaboratively without exposing raw datasets. Google’s Private Join and Compute is a practical implementation of this approach. It enables privacy-preserving comparisons across datasets from different organizations and has already been used in joint analytics projects in public health and demographic research.

4. Internet of Things (IoT) Security

IoT devices continuously collect data from homes, factories, hospitals, and infrastructure systems. This data, if compromised, can reveal sensitive personal behavior, industrial performance metrics, or operational vulnerabilities. Intel’s Homomorphic Encryption Toolkit equips developers to integrate secure processing directly into IoT applications. As a result, sensor data can be analyzed for patterns or system alerts without exposing the underlying raw values, improving data protection across connected environments.

6. Protected Data Sharing in Research

Scientific and medical research often depends on collaboration between institutions, yet data privacy rules can restrict how information is shared. Duality Technologies addresses this challenge with its SecurePlus platform, which facilitates encrypted collaboration across genomic and financial research datasets. Running on Oracle Cloud Infrastructure, the platform enables researchers to perform computations on encrypted data while meeting regulatory standards, making cross-border and multi-institutional studies more viable.

Utilizing Homomorphic Encryption in Identity Verification

In digital identity systems, privacy concerns often arise when verifying user attributes. Homomorphic encryption offers a way to perform checks without disclosing underlying personal details. This is especially useful for verifying criteria such as age, residency, or eligibility without revealing the complete identity profile.

For instance, a service that needs to enforce age-based restrictions can confirm whether a user meets the requirement without accessing their actual birthdate. The platform receives only the outcome of the check—such as a yes or no—without handling any unnecessary personal information.

This method minimizes exposure risk and helps organizations align with privacy regulations like the General Data Protection Regulation. It also reduces the burden of managing sensitive data and limits liability for compliance breaches.

By enabling secure attribute verification between entities, this approach supports safer cross-platform collaboration, enhances user privacy, and strengthens trust in identity verification systems.

What Are the Challenges of Homomorphic Encryption?

Homomorphic encryption offers powerful capabilities for privacy-preserving data processing. However, the technology still faces several challenges that can affect its practical use. These constraints should be considered carefully when evaluating whether homomorphic encryption is the right fit for a specific application.

1. High Computational Demands

Fully Homomorphic Encryption (FHE), which supports unrestricted computations on encrypted data, can be extremely resource-intensive. This level of complexity often leads to significant performance overhead, making FHE impractical for real-time systems or environments with limited processing power. For example, processing large datasets with FHE may require specialized hardware and long processing times.

2. Limited Operation Support in Some Schemes

Not all homomorphic encryption schemes support full functionality. Partially Homomorphic Encryption (PHE) allows only one type of operation, such as addition or multiplication, while Somewhat Homomorphic Encryption (SHE) supports both but only for a limited number of times. Although FHE overcomes these restrictions, its performance requirements can limit its widespread adoption.

3. Data Expansion

Encrypted data in homomorphic schemes is often much larger than its original form. This data expansion increases storage requirements and bandwidth usage, which can drive up infrastructure costs. For example, encrypting a full database with FHE could lead to significant increases in file size, making it more difficult to transmit or store efficiently.

4. Security Assumptions and Emerging Risks

Homomorphic encryption relies on the assumption that certain mathematical problems are hard to solve. If those assumptions are weakened—especially by emerging technologies like quantum computing—some encryption schemes could become vulnerable. While earlier schemes may be susceptible to quantum attacks, modern implementations of FHE are designed to be resistant. The Homomorphic Encryption Security Standard, published in 2018, outlines the expected levels of quantum resilience for different schemes.

5. Performance Constraints in Real-Time Applications

Despite advances in efficiency, homomorphic encryption is still not well-suited for applications that require low latency or immediate response times. Interactive systems, live data processing, or streaming environments may struggle to meet performance expectations when using HE due to its computational overhead.

The Future of Homomorphic Encryption

Despite current limitations, homomorphic encryption continues to evolve. Ongoing research, industry adoption, and technological innovation are pushing the boundaries of what is possible. Below are key developments shaping the future of this technology.

1. Research Advancements

Significant academic and private-sector research is focused on making homomorphic encryption more efficient. Work is underway to reduce processing time, memory usage, and data size through improved algorithms and new implementation techniques. These efforts are bringing the technology closer to real-world viability for broader use cases, including machine learning, edge computing, and mobile applications.

2. Industry Adoption and Market Demand

Organizations are under increasing pressure to extract value from their data while complying with stricter privacy regulations. Sectors such as healthcare, finance, and government are leading adoption efforts. For example, cloud service providers are exploring ways to offer homomorphic encryption as a privacy layer, allowing clients to compute on encrypted data without ever accessing the raw content. This is particularly valuable for third-party analytics or remote processing.

3. Remaining Barriers and Ongoing Challenges

Balancing security with performance remains a major challenge. As computations grow in complexity, the computational load and memory requirements can quickly increase. Researchers are actively addressing this issue by developing more efficient encryption schemes and hybrid approaches that combine homomorphic encryption with other privacy-enhancing technologies. Practical deployment at scale will require continued progress in reducing cost, complexity, and integration hurdles.

Identity.com

Identity.com, as a future-oriented company, is helping many businesses by giving their customers a hassle-free identity verification process. Our organization envisions a user-centric internet where individuals maintain control over their data. This commitment drives Identity.com to actively contribute to this future through innovative identity management systems and protocols.

As members of the World Wide Web Consortium (W3C), we uphold the standards for the World Wide Web and work towards a more secure and user-friendly online experience. Identity.com is an open-source ecosystem providing access to on-chain and secure identity verification. Our solutions improve the user experience and reduce onboarding friction through reusable and interoperable Gateway Passes. Please get in touch for more information about how we can help you with identity verification and general KYC processes.