Unlocking Secure Data Processing

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In today's data-driven world, the need to balance security, privacy, and efficiency has become paramount. Traditional encryption methods secure data at rest and during transmission (End-to-End Encryption), but they require decryption for processing—introducing vulnerabilities. Homomorphic Encryption (HE) closes this gap by enabling computations directly on encrypted data, producing results that remain encrypted until securely decrypted by authorized users.

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Our proprietary Fully Homomorphic Encryption (FHE) scheme revolutionizes this field by offering significant improvements in efficiency and applicability. Based on a novel "random change of variables" method, this scheme overcomes common challenges like noise, time delays, and high energy consumption, making it adaptable to real-world applications across industries.

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Why Homomorphic Encryption is Important

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• Enhanced Privacy and Security
  With HE, sensitive data never needs to be decrypted during processing. This ensures complete data privacy and security, even when handled by third-party systems or cloud environments. For example:

  • Banks can process loan applications without exposing financial details.

  • AI models can be trained on encrypted datasets without revealing sensitive information.

• Regulatory Compliance
  HE aligns with stringent data privacy regulations like GDPR and HIPAA, as it guarantees the confidentiality of personal and sensitive information throughout its lifecycle.

• Secure Cloud Computing
  As cloud adoption grows, businesses are hesitant to fully trust external providers with sensitive data. HE enables organizations to leverage cloud computing's scalability and cost-effectiveness without compromising security.

• Applications Across Diverse Industries

  • Finance: Securely evaluate encrypted financial transactions, ensuring no sensitive information is exposed.

  • Healthcare: Perform computations on encrypted medical records for diagnostics or research, protecting patient confidentiality.

  • Artificial Intelligence and Machine Learning: Train and deploy models using encrypted data, avoiding data breaches or misuse.

  • Government and Defense: Securely process classified or sensitive data without risking exposure.

 

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The Benefits of Our FHE Scheme

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• Solves Noise and Efficiency Challenges
  Traditional HE methods often face issues with computational noise and inefficiencies. Our method eliminates these hurdles, enabling high-precision computations without significant trade-offs in speed or energy use.

• Flexibility and Scalability
  Our HE solution can be tailored to various security requirements, allowing businesses to integrate it seamlessly into existing workflows and scale it as needed.

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Impact and Market Potential

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The exponential growth of data, coupled with increasing cyber threats, has created an urgent demand for robust encryption solutions. By enabling secure data processing without decryption, Homomorphic Encryption is poised to redefine data security and unlock new possibilities in the age of AI and cloud computing.

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• The global market for homomorphic encryption is projected to grow rapidly, fueled by its critical role in data privacy and security.

• Early adoption provides businesses with a competitive edge, not only through compliance and risk reduction but also by unlocking new opportunities in secure data-driven innovation.

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1. Proposal

2. Proof-of-Concept

   • White Paper

   • Documentation

     • User Guide

     • Developer's Manual

3. Conference

   • Presentation

   • Report

4. Additional Links
   • ​General Theory of Homomorphic Encryption

     • On Data Banks and Privacy Homomorphisms (1978)​

     • A FHE Scheme - Gentry (2009)

     • HE (2013)

     • Graph Theory for Cryptographic Boolean Functions​

     • What is HE and How it is Used

     • A Decade (or so) of FHE, by Craig Gentry

     • Demystifying Bootstrapping in FHE

     • Programmable Bootstrapping Enables Efficient Homomorphic Inference of NN

     • Quantum HE for Polynomial-Sized Circuits

     • Inferati-Zero Trust AI

   • Functional HE Schemes
     • Microsoft
       • Microsoft SEAL: Fast and Easy-to-Use Homomorphic Encryption Library
       • Microsoft SEAL is an Easy to Use and Powerful HE Library
       • A Library for doing HE Operations on Tensors
     • Introduction to the CKKS Encryption Scheme
     • HElib is an Open-Source Software Library that Implements HE
     • Lattice Cryptography
       • Lattice Based Cryptography and FHE
       • PALISADE
       • Implementation of Lattices System in HE
     • iDash
       • iDash Privacy and Security Workshop 2024
       • The iDash Competition
     • Framework for Compiling C++ Programs with Encrypted Operands