Table of Contents
- 1 Key Takeaways:
- 2 What Is Homomorphic Encryption?
- 3 The Need For Homomorphic Encryption
- 4 Limitations of Traditional Encryption Techniques
- 5 How Homomorphic Encryption Works
- 6 Types of Homomorphic Encryption
- 7 Encryption Types: A Table Comparison
- 8 Applications of Homomorphic Encryption
- 9 Utilizing Homomorphic Encryption in Identity Verification
- 10 Limitations of Homomorphic Encryption
- 11 The Future of Homomorphic Encryption
- 12 Identity.com
Key Takeaways:
- Homomorphic encryption is a encryption technique that allows you to perform mathematical operations on encrypted data without ever decrypting it.
- Since the data remains encrypted throughout the process, homomorphic encryption offers a significant privacy advantage, protecting sensitive information from potential breaches.
- This encryption technique allows organizations to securely outsource computation tasks to third-party providers without revealing the actual data content.
Encryption technology is a major advancement in information and computerized technology, transforming data transmission, protection, and privacy. It works by transforming plaintext data into ciphertext, making it unreadable to anyone without the corresponding decryption key. Encryption greatly enhances data security, confidentiality, and integrity by preventing unauthorized access to sensitive information. Despite these benefits, the requirement for decryption before data processing remains a critical concern. This step introduces new risks to privacy and confidentiality, challenging the core benefits of encryption. A solution to this challenge is seen in homomorphic encryption, an advanced type of encryption that allows computation on encrypted data without the need to decrypt it first.
What Is Homomorphic Encryption?
Homomorphic encryption is a method that allows mathematical computations to be carried out on encrypted data (ciphertext) without needing to decrypt it. Derived from the Greek word “homomorphic,” meaning “same structure,” this technology enables operations to be performed while maintaining the data’s encrypted state.
By using homomorphic encryption, if decrypted, the results from computations on encrypted data will match those from similar operations performed on the original, unencrypted data (plaintext). This capability allows data to remain encrypted and secure while being processed, enabling third parties to handle sensitive information without compromising its confidentiality. As a result, homomorphic encryption ensures that the results are both valid and usable for their intended purpose. This revolutionizes the way sensitive data is handled in environments where privacy is paramount.
The Need For Homomorphic Encryption
In cloud environments, organizations typically use traditional encryption to protect sensitive data. However, when the need arises to analyze or verify this encrypted data in the cloud, they face two options: decrypt the data on-cloud, risking security breaches, or decrypt it off-cloud, which can be costly and time-consuming.
Homomorphic encryption overcomes these challenges by enabling organizations to securely share and process private data without compromising privacy. It allows mathematical operations to be performed on encrypted data without exposing the underlying plaintext data.
Limitations of Traditional Encryption Techniques
Data can exist in three main states: stored (at rest, e.g., a file on your hard disk), in transit (e.g., a PDF attachment sent via email), or in use (e.g., accessing a webpage in real-time). Traditional encryption effectively protects data when it is stored or in transit by preventing unauthorized access. However, it falls short when data needs to be used in real-time. Homomorphic encryption is one of the few developments addressing this issue, ensuring that privacy and confidentiality are maintained even when data is actively used or processed.
Below are some of the limitations of traditional encryption techniques:
- Data usability: Traditional encryption methods require data to be decrypted before it can be used or processed. This decryption process introduces security risks as the data is exposed in its unencrypted form during processing.
- Data privacy: Once data is decrypted for processing, it loses confidentiality and becomes vulnerable to unauthorized access. This is especially problematic in scenarios requiring multiple parties to share or process data.
- Scalability: In situations where data needs to be shared among multiple parties who prioritize privacy and confidentiality, each party typically encrypts the data again after processing before passing it on. This cycle of encryption and decryption can become impractical with large data volumes or complex computations, as decrypting and re-encrypting data is computationally intensive and time-consuming.
- Data integrity: While encryption protects data from unauthorized access, it does not protect against data corruption or tampering. In the unencrypted state, data can easily be corrupted, or new false data can be introduced.
How Homomorphic Encryption Works
The fundamental principle of homomorphic encryption is to encrypt data in a manner that still allows mathematical operations to be performed on the encrypted data. This powerful tool ensures privacy and security in data processing and analysis, especially when third-party services handle sensitive data. Below, we outline the three basic steps involved in homomorphic encryption:
Encryption
The data owner uses a homomorphic encryption scheme to encrypt the data. This process transforms the original data into ciphertext that conceals the data yet still permits specific computations to be carried out on it.
Computation
A third party, such as a cloud service provider, receives the encrypted data and performs computations on it without the need to decrypt it. Homomorphic encryption schemes generally involve addition and multiplication operations. These operations support both real numbers and integers and are the most commonly supported operations.
Decryption
After the computations are completed, the third party sends the encrypted result back to the data owner. The data owner then uses their private key to decrypt the result, revealing the processed data.
Types of Homomorphic Encryption
Homomorphic encryption is categorized into three types based on the mathematical computations that can be performed on ciphertext. Each type offers different levels of functionality and security.
Partially Homomorphic Encryption (PHE)
Somewhat Homomorphic Encryption (SHE)
SHE schemes permit a limited number of homomorphic operations, including both addition and multiplication, on encrypted data (ciphertext). This is more versatile than PHE, which supports unlimited operations but only of one kind.
Fully Homomorphic Encryption (FHE)
FHE enables an unlimited number of both homomorphic addition and multiplication operations on encrypted data. This allows for highly complex computations on encrypted data. Examples include the Brakerski-Fan-Vercauteren (BFV) scheme. Although FHE schemes are computationally and memory demanding, making them challenging to implement, they are extremely powerful and valuable for privacy-preserving data aggregation services.
Encryption Types: A Table Comparison
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:
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.
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.
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.
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.
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.
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:
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.
Limited Functionality
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.
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.
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
What does the future hold for homomorphic encryption, given its significant value to the field of encryption and its current limitations? Here are three perspectives on this question:
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.
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.
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.