Skip to content
Technology

Homomorphic Encryption Defined: A Method for Protecting Data During Processing While Keeping It Encrypted.

Encrypted data can undergo computations without initial decryption due to the application of homomorphic encryption.

,   and  Administrator
5 min read
Performing calculations on encrypted information is possible with the application of homomorphic...
Performing calculations on encrypted information is possible with the application of homomorphic encryption, without the need for initial decryption.

Homomorphic Encryption Defined: A Method for Protecting Data During Processing While Keeping It Encrypted.

Homomorphic Encryption: A Revolutionary Solution for Secure Data Processing

data privacy, cloud computing, encryption, homomorphic encryption, secure data processing

Data privacy has been a critical concern in the digital age, with sensitive information being stored and processed in various platforms, including the cloud. Homomorphic encryption, a transformative encryption technology, provides a groundbreaking solution to ensure secure data processing without compromising privacy.

Homomorphic encryption allows computations to be carried out on encrypted data, eliminating the need for decryption during the data processing phase. This technological innovation empowers third-party servers to process encrypted data without accessing raw, unencrypted information. As a result, it is an ideal fit for cloud computing environments that prioritize data privacy and compliance with regulations such as GDPR and HIPAA.

Understanding Homomorphic Encryption

Types of Homomorphic Encryption

Homomorphic encryption preserves the structure of ciphertext data, allowing secure operations without disclosing contents. The name is derived from the Greek term "homomorphic," which means "same structure." With homomorphic encryption, data remains secure while still enabling secure processing and analysis of sensitive information.

Operations

The fundamental process of homomorphic encryption consists of three steps: encryption, computation, and decryption. Data owners encrypt their data using a homomorphic encryption algorithm, transforming it into ciphertext for secure processing. Third-party servers can then perform computations on encrypted data without needing decryption. Resulting encrypted data can be sent back to the data owner, who can decrypt the final processed information with a private key.

Number of Operations

Classifying Homomorphic Encryption

Homomorphic encryption is classified into three main types: Partially Homomorphic Encryption (PHE), Somewhat Homomorphic Encryption (SHE), and Fully Homomorphic Encryption (FHE).

Partially Homomorphic Encryption (PHE)

  1. Partially Homomorphic Encryption (PHE) enables only one specific mathematical operation (either addition or multiplication) on ciphertext data.
  2. Somewhat Homomorphic Encryption (SHE) allows both addition and multiplication, but only within a limited number of operations.
  3. Fully Homomorphic Encryption (FHE) is the most advanced type, offering the capability to carry out an unlimited number of both addition and multiplication operations on ciphertext data, making it suitable for complex computations.

One (addition or multiplication)

Overcoming Challenges of Traditional Encryption

Unlimited

Traditional encryption effectively protects data during storage and transmission but introduces risks when data needs to be actively used in real-time processing. Homomorphic encryption addresses these challenges by performing calculations directly on encrypted data, safeguarding it during processing and addressing the following limitations of traditional encryption:

  • Data Usability: Traditional encryption necessitates data decryption before processing, exposing it to potential risk. Homomorphic encryption eliminates this requirement, helping to maintain data security.
  • Data Privacy: Keeping data encrypted throughout the computation process ensures it retains confidentiality, shielding sensitive information from unauthorized access.
  • Scalability: Homomorphic encryption enables larger datasets and complex computations to be processed without the need for constant decryption and encryption, enhancing efficiency.
  • Data Integrity: Homomorphic encryption preserves data integrity by keeping it encrypted during real-time operations, forestalling tampering or corruption.

Somewhat Homomorphic Encryption (SHE)

Applications of Homomorphic Encryption

Two (addition and multiplication)

The applications of homomorphic encryption span various industries, with a few examples listed below:

Limited

  1. Secure Cloud Computing: Homomorphic encryption safeguards sensitive data while enabling cloud service providers to securely process data on behalf of users. For instance, healthcare providers utilize homomorphic encryption to process patient data in the cloud.
  2. Privacy-Preserving Data Analysis: Organizations can analyze sensitive data without exposing it by using homomorphic encryption. An example is financial institutions identifying increased fraudulent activity within encrypted transaction data.
  3. Secure Multi-Party Computation: IoT devices employ homomorphic encryption to share encrypted data securely and transparently, ensuring privacy during data processing.
  4. Privacy-Preserving Internet of Things (IoT): Homomorphic encryption enables IoT devices to securely transmit and process sensitive data without compromising privacy.
  5. Secure Voting Systems: Encrypted votes can be tallied without breaching voter confidentiality, addressing the security concerns associated with traditional paper voting systems.
  6. Secure Data Sharing and Collaboration: Researchers can collaborate securely on encrypted data, particularly in fields like genomic data studies.

Integrating Homomorphic Encryption into Identity Verification

Fully Homomorphic Encryption (FHE)

Homomorphic encryption significantly enhances the privacy and security of our website verification processes by enabling third-party services to perform necessary computations on encrypted data without accessing raw, unencrypted information. This approach:

Two (addition and multiplication)

  1. Minimizes the risk of data exposure during verification processes.
  2. Facilitates seamless interactions between entities.
  3. Meets stringent data protection regulations like GDPR.

Unlimited

Limitations of Homomorphic Encryption

While homomorphic encryption offers several advantages for secure data processing, it also faces critical challenges that may impact practical usage:

  1. Computational Complexity: FHE, the advanced form of homomorphic encryption, can be computationally demanding, potentially causing performance issues for resource-constrained applications.
  2. Limited Functionality: Some homomorphic encryption schemes, like PHE and SHE, restrict the types of computations they support.
  3. Data Expansion: Homomorphic encryption can lead to data expansion, resulting in increased storage and transmission costs.
  4. Security Assumptions: FHE depends on certain security assumptions that may be compromised by advancements in quantum computing.
  5. Use Case Limitations: Homomorphic encryption may not be suitable for real-time applications that require low latency or minimal response time.

The Future of Homomorphic Encryption

The future of FHE is promising due to ongoing research advancements and growing interest from various industries. Applications in AI, Web3, and broader data security arenas are expected to drive the adoption of homomorphic encryption. Researchers are focusing on enhancing efficiency, security, and scalability to make FHE more practical for real-world applications. However, finding a balance between security and efficiency remains a critical challenge that the field must address to fully realize the potential of homomorphic encryption.

Related Developments in Homomorphic Encryption

  1. Nvidia-based Homomorphic Encryption: Lattica's recent efforts have led to the development of Nvidia-based homomorphic encryption, enabling AI models to operate on encrypted data without exposing its contents.
  2. Mind Network Integration with Web3 and AI: Mind Network is striving to integrate FHE into Web3 and Agentic AI stacks by implementing advanced cryptography, real-time monitoring, and audits to mitigate threats like ciphertext malleability and noise growth, aiming to make FHE more mainstream.
  3. Mathematical Foundations and Efficiency Improvements: Researchers are focusing on improving efficiency using Learning with Errors (LWE) and Ideal-Lattice techniques, with schemes like BGV and FV showing promise for better performance.
  4. Security and Scalability Enhancements: Integrating post-quantum cryptography principles into FHE is essential for safeguarding long-term security, along with multilayered security protocols to protect sensitive information in complex systems.

[1] https://medium.com/lattica/deep-learning-on-encrypted-data-5-secret-ingredients-7c7167c908e8[2] https://medium.com/@MindNetwork_OpenSource/mind-networks-static-encrypted-web3-framework-7c9fdc44fc04[3] https://arxiv.org/abs/1702.01237[4] https://eprint.iacr.org/2021/366.pdf[5] https://www.sciencedirect.com/science/article/pii/B9780128123898000074

Homomorphic encryption, as a revolutionary technology in data-and-cloud-computing, enables secure data processing without sacrificing privacy. This technology empowers third-party servers to perform computations on encrypted data, thus ensuring the privacy of sensitive information in cloud computing environments.

The applications of homomorphic encryption span various industries, particularly in secure cloud computing, privacy-preserving data analysis, and secure multi-party computation. For instance, healthcare providers utilize homomorphic encryption to process patient data in the cloud, while financial institutions analyze sensitive data without exposing it for fraud detection purposes.

Read also:

    Related

    Latest