What Is Homomorphic Encryption?

Key Takeaways:

• Homomorphic encryption is a type of encryption that allows computations to be performed on encrypted data without decrypting it first. This ensures the data remains secure and private while being processed.
• By keeping data encrypted throughout the process, homomorphic encryption offers a significant privacy advantage, protecting sensitive information from potential breaches.
• Homomorphic encryption allows organizations to securely outsource computation tasks to third-party providers without revealing the actual data content.

 

Encryption technology has significantly transformed data transmission, protection, and privacy by converting plaintext data into ciphertext, making it unreadable to unauthorized individuals without the decryption key. This transformation greatly enhances data security, confidentiality, and integrity. However, the requirement for decryption before data processing introduces risks to privacy and confidentiality, which challenges the core benefits of encryption. Homomorphic encryption offers a solution by allowing computations on encrypted data without the need for decryption.

What Is Homomorphic Encryption?

Homomorphic encryption is a type of encryption that allows computations to be performed on encrypted data without decrypting it first. After decryption, the results of these computations are identical to those obtained if the operations were performed on unencrypted data. This approach enables secure processing and analysis of sensitive information while ensuring data confidentiality.

The Need For Homomorphic Encryption

In cloud environments, organizations often use traditional encryption to protect sensitive data. However, when they need to analyze or verify this data in the cloud, they face two problematic options: decrypt the data on the cloud, risking security breaches, or decrypt it off-cloud, which can be costly and time-consuming. Homomorphic encryption addresses these challenges by enabling secure data sharing and processing without compromising privacy. This technology allows operations on encrypted data without exposing the underlying plaintext, ensuring data remains secure even during computation.

Limitations of Traditional Encryption Techniques

Data can exist in three main states: stored (at rest), in transit, or in use. Traditional encryption protects data when stored or in transit but falls short when data needs to be used in real-time. Homomorphic encryption addresses this issue by maintaining privacy and confidentiality even when data is actively used or processed. The limitations of traditional encryption techniques include:

• Data usability: Traditional encryption requires data to be decrypted before use or processing, exposing it to security risks during processing.
• Data privacy: Once decrypted for processing, data loses confidentiality and becomes vulnerable to unauthorized access.
• Scalability: The cycle of encryption and decryption can become impractical with large data volumes or complex computations.
• Data integrity: Encryption protects against unauthorized access but does not prevent data corruption or tampering in its unencrypted state.

How Homomorphic Encryption Works

Homomorphic encryption enables mathematical operations to be performed on encrypted data, ensuring privacy and security even when third-party services handle sensitive information. This technology is particularly valuable in scenarios where data confidentiality is critical. The three basic steps involved in homomorphic encryption are: encryption, computation, and decryption.

1. Encryption: The data owner first encrypts the data using a homomorphic encryption algorithm. This process converts the original data into ciphertext, ensuring that the data is concealed but still allows specific computations to be performed on it.
2. Computation: A third party, such as a cloud service provider, can perform computations on the encrypted data without decrypting it. Homomorphic encryption typically supports fundamental operations such as addition and multiplication, making it versatile for both real numbers and integers.
3. Decryption: Once the computations are complete, the encrypted result is sent back to the data owner. The data owner then decrypts the result using their private key, revealing the processed information in its final form.

The Three Types of Homomorphic Encryption

Homomorphic encryption comes in three main types: Partially Homomorphic Encryption (PHE), Somewhat Homomorphic Encryption (SHE), and Fully Homomorphic Encryption (FHE).

1. Partially Homomorphic Encryption (PHE)

2. Somewhat Homomorphic Encryption (SHE)

Somewhat Homomorphic Encryption (SHE) permits both addition and multiplication on encrypted data, but only for a limited number of operations. While more versatile than PHE, SHE’s limited capacity makes it suitable for certain specific applications where only a few operations are required.

3. Fully Homomorphic Encryption (FHE)

Fully Homomorphic Encryption (FHE) is the most advanced type, allowing an unlimited number of both addition and multiplication operations on encrypted data. This capability supports complex computations without needing to decrypt the data. However, FHE is resource-intensive, demanding significant computational power and memory. Despite these challenges, FHE is highly valuable in privacy-preserving data aggregation services, with examples like the Brakerski-Fan-Vercauteren (BFV) scheme offering robust solutions for secure data processing.

 

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

 

Applications of Homomorphic Encryption

Homomorphic encryption has several practical applications across various industries and sectors. Below are a few examples:

1. Secure Cloud Computing

Homomorphic encryption allows data to be stored and processed in the cloud without revealing the underlying plaintext. This capability enables the secure outsourcing of computation tasks to cloud service providers. For instance, a healthcare provider could use homomorphic encryption to securely store and process patient data in the cloud, ensuring privacy and confidentiality.

2. Privacy-Preserving Data Analysis

Organizations can use homomorphic encryption to perform data analysis on sensitive information without exposing the data. For example, financial institutions might analyze encrypted transaction data to detect patterns and identify increases in fraudulent activity, all without decrypting the data.

3. Secure Multi-Party Computation

IoT devices can use homomorphic encryption to securely transmit and process data without revealing sensitive information. For example, a smart home system could analyze encrypted sensor data to detect usage patterns while keeping the raw sensor readings confidential.

4. Privacy-Preserving Internet of Things (IoT)

IoT devices can use homomorphic encryption to securely transmit and process data without revealing sensitive information. For example, a smart home system could analyze encrypted sensor data to detect patterns without exposing the raw sensor readings.

5. Secure Voting Systems

Homomorphic encryption can be used to build secure electronic voting systems where encrypted votes can be tallied without revealing individual voter choices, ensuring the integrity and confidentiality of the voting process. This method addresses the security issues associated with traditional paper voting systems, including ballot tampering and manipulation by malicious actors.

6. Secure Data Sharing and Collaboration

Homomorphic encryption facilitates the secure sharing and collaboration on encrypted data. For example, researchers could share encrypted genomic data for collaborative studies while ensuring the privacy of patient information.

Utilizing Homomorphic Encryption in Identity Verification

Homomorphic encryption significantly enhances the privacy and security of identity verification processes by enabling third-party services to perform necessary computations on encrypted identity data without accessing the underlying plaintext. This capability is especially crucial in maintaining data integrity during verification processes. For instance, if a service needs to verify a user’s age to ensure compliance with age-specific regulations, homomorphic encryption allows for calculations to be performed directly on encrypted data—such as verifying whether the age meets a certain criterion—without the need to decrypt or access the user’s full identity information.

Limitations of Homomorphic Encryption

While homomorphic encryption provides strong capabilities for secure and privacy-preserving computation, it also has several limitations that can impact its practical usage. These limitations should be carefully considered when evaluating its suitability for specific applications or use cases:

1. Computational Complexity

Homomorphic encryption schemes, particularly Fully Homomorphic Encryption (FHE), which allows for unrestricted calculations on encrypted data, can be computationally intensive. This complexity may result in significant performance burdens, making it unsuitable for real-time or resource-constrained applications. For example, using FHE to process large datasets or complex computations can require substantial computing resources and time.

2. Limited Functionality

3. Data Expansion

Homomorphic encryption often results in data expansion, where the size of the encrypted data exceeds that of the plaintext. This can lead to increased storage and transmission costs, particularly for large datasets. For instance, encrypting a large database with homomorphic encryption can significantly increase storage requirements, transmission costs, and bandwidth usage, making its use less feasible where storage and bandwidth are limiting factors.

4. Security Assumptions

Homomorphic encryption is dependent on specific security assumptions, such as the difficulty of solving certain mathematical problems. If these assumptions are compromised, the encryption scheme may become vulnerable. For example, advances in quantum computing could potentially break the security assumptions underlying certain types of homomorphic encryption, rendering them insecure. Notably, some older forms of homomorphic encryption are vulnerable to quantum attacks. Fully Homomorphic Encryption (FHE) is considered resistant, as detailed in the Homomorphic Encryption Security Standard released on November 21, 2018.

5. Use Case Limitations

Homomorphic encryption may not be suitable for all use cases, especially those that require real-time processing or low latency (low delay). For example, applications that require immediate response times, such as real-time data processing or interactive systems, may not be well-suited for homomorphic encryption due to its computational overhead.

The Future of Homomorphic Encryption

The future of homomorphic encryption looks promising as it continues to attract significant research interest and industry application due to its ability to secure data while allowing computations. Here are three perspectives on this question:

1. Research Advancements

The future of homomorphic encryption is promising, with continuous research advancements and growing interest from various industries. Numerous research papers across the internet focus on improving different aspects of homomorphic encryption schemes. A primary focus for future development is reducing the computational and memory overhead associated with this technology. Researchers are developing new algorithms and optimization techniques to make homomorphic encryption more efficient and practical for real-world applications.

2. Industry Trends

Cloud storage is a cost-effective option for organizations dealing with growing data volumes. However, stricter data privacy regulations have forced many organizations to choose between hosting data in-house or finding a way to use third-party storage providers while keeping data confidential. This dilemma has led to increased interest in privacy-preserving technologies like homomorphic encryption. Especially in sectors handling sensitive data, such as healthcare, finance, and government. As data breaches and privacy concerns continue to make headlines, organizations are seeking solutions that protect data without breaking the bank while still allowing for valuable insights to be extracted. By allowing computations on encrypted data without compromising privacy, even when performed by a third party, homomorphic encryption presents a practical solution for these organizations.

3. Potential Challenges

While homomorphic encryption offers substantial benefits in combating data breaches and unauthorized access, it also faces several challenges. One of the main challenges is achieving a balance between security and efficiency. As more complex computations are performed on encrypted data, the computational and memory requirements can become prohibitive. Researchers are working to address these challenges by developing new algorithms and techniques that strike a better balance between security, storage, and efficiency.

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.