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Abstract

Today, all existing Fully Homomorphic Encryption (FHE) schemes have 6 main limitations:

  • Huge computational and memory overhead
  • Huge ciphertext expansion
  • The security layer is provided by the presence of noise
  • Usage of bootstrapping mechanism when the noise grows
  • There is no possibility to use 'division'
  • Limited amount of consequent arithmetic operations, basically up to 20, due to noise growth

We introduce a fundamentally different and groundbreaking approach to Fully Homomorphic Encryption (FHE) with new mathematical principles. In our system, homomorphism is a native property and not a forced mechanism applied to non-homomorphic elements, which sets us apart from all existing schemes.

We are building a new era of security FOR YOU

Financial Services

Healthcare

Cloud Computing

Automotive

Machine Learning & Data Analysis

Government & Defence

Databases

Use Cases

MathLock fundamentally changes FHE's operational capabilities and performance. We've built revolutionary and practical FHE cryptosystem with unmatched performance and security.

Financial Services

We suggest Global Transformation of Transaction Systems in the Financial Services

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Healthcare

Advancing Patient Privacy and Data Security with Homomorphic Encryption

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Cloud Computing

Secure Cloud Computations, revolutionizing Data Security with Homomorphic Encryption

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Automotive

Advancing Automotive Data Security and Privacy with Homomorphic Encryption

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Machine Learning & Data Analysis

Empowering Privacy-Preserving Insights with Homomorphic Encryption

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Government & Defence

Fortifying National Security with Homomorphic Encryption

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Databases

Homomorphic encryption is a game-changing technology in the world of databases

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Overall

Homomorphic encryption emerges as a critical enabler in diverse industries, addressing the shared concern of data security and privacy. It revolutionizes how sensitive information is handled and analyzed, while maintaining confidentiality through encrypted data processing.


Red Lines:

  • Secure Data Processing: Homomorphic encryption allows for data processing while data remains encrypted. This ensures that sensitive information is protected at all times.
  • Privacy-Preserving Analytics: Data analysis can be performed on encrypted data, avoiding the need for data decryption and preserving privacy.
  • Compliance with Regulations: Homomorphic encryption aids in meeting strict data privacy regulations (e.g., HIPAA, GDPR) while conducting valuable analyses.
  • Collaboration and Outsourcing: Organizations can share encrypted data securely and collaborate without revealing raw data, while also utilizing cloud resources and third-party services without compromising privacy.
  • Data Integrity and Non-Repudiation: Homomorphic hashes ensure the integrity of data during various operations, preventing unauthorized modifications.
  • Confidential Communications: It enables secure communication between entities, ensuring that sensitive information remains protected.
  • Insider Threat Mitigation: Homomorphic encryption prevents unauthorized access to specific data and protects against insider threats.
  • Preserving Individual Privacy: In healthcare and other sectors, homomorphic encryption safeguards individual data privacy, allowing for collaborative research without compromising patient identities.
  • Advancing Security in AI: By securing data used in AI-driven applications, it fosters trust in the deployment of AI systems.
  • Empowering Data-Driven Insights: Homomorphic encryption facilitates data analytics, providing valuable insights while maintaining the highest level of security.

    • Overall, homomorphic encryption represents a paradigm shift in secure data processing, addressing the critical need for data privacy and confidentiality in the digital age. By embracing this transformative technology, industries can confidently unlock the potential of data-driven innovations while safeguarding individual privacy and upholding data security standards.