Zero-Knowledge Secure LLM

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Almalgo
Project Owner

Zero-Knowledge Secure LLM

Funding Requested

$125,000 USD

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Overview

We plan to use homomorphic encryption (HE) to enable zero-knowledge security in Large Language Models (LLMs) on SingularityNet's decentralized AI platform. It involves modifying LLMs to process encrypted data, and maintaining confidentiality throughout. The system uses distributed computing, optimizing performance via hardware accelerations and batching. Security is reinforced with regular audits and zero-knowledge proofs to meet global standards like GDPR, ensuring scalable, secure AI applications.

Proposal Description

Company Name (if applicable)

Almalgo

How our project will contribute to the growth of the decentralized AI platform

A zero-knowledge secure LLM on SingularityNet enhances trust and compliance, making the platform appealing to industries needing data privacy. It promotes a secure collaborative environment, driving innovation and attracting a broad user base. By integrating this technology, SingularityNet could become a leader in privacy-focused AI, fostering a richer, more secure AI ecosystem.

The core problem we are aiming to solve

Our project primarily aims to solve the problem of data privacy and security in AI operations. In traditional AI models, sensitive data must often be exposed to the model during training and inference, creating significant privacy risks and compliance challenges. This can hinder adoption, especially in industries dealing with confidential information like healthcare, finance, and legal sectors. A ZK secure LLM addresses this by enabling the model to perform computations and generate insights without ever accessing the actual data. It uses cryptographic techniques to ensure that the model proves its operations are correct without revealing or even accessing the underlying data. This approach not only secures user data but also builds trust, allowing for wider adoption of AI technologies across various privacy-sensitive domains. By ensuring that data remains encrypted and inaccessible throughout AI processing, a ZK secure LLM facilitates compliance with strict data privacy regulations.

Our specific solution to this problem

Our proposed solution involves integrating homomorphic encryption into the backend calculations of a Large Language Model (LLM) to ensure zero-knowledge security. Homomorphic encryption allows the LLM to perform operations on encrypted data without ever decrypting it. This means the model can analyze, learn from, and generate responses based on data that remains in a secure, encrypted state throughout the processing. This technique addresses key privacy concerns by ensuring that sensitive information is not exposed, even to the model itself. By using this advanced cryptographic method, the LLM can safely be used in environments where data privacy is paramount, such as in healthcare or finance, without compromising functionality. This secure approach not only enhances user trust but also complies with stringent data protection regulations, thereby broadening the practical applications of AI in sensitive and critical domains.

Project details

The solution proposed involves leveraging homomorphic encryption as a core technology to enable zero-knowledge security within a Large Language Model (LLM) framework, specifically tailored for deployment on SingularityNet's decentralized AI platform. Here's a detailed breakdown of the technical specifications and technology details:

1. Homomorphic Encryption (HE) Implementation

Homomorphic encryption allows data to be encrypted in such a way that it can still be processed by an LLM without decryption. The LLM performs operations directly on ciphertexts—encrypted versions of the data—thus ensuring that the underlying data remains confidential throughout the computation process. We could utilize specific HE schemes like CKKS (Cheon-Kim-Kim-Song) for handling real numbers, which is essential for the nuanced processing LLMs require.

2. Integration with Large Language Models

The LLM can be structured to operate in a layer-wise manner where each layer of the neural network is capable of processing encrypted inputs and generating encrypted outputs. This modification ensures that all internal states of the LLM remain encrypted, and only final, relevant outputs are decrypted, if necessary, by the client side with appropriate keys. For instance, transforming a BERT or GPT model to support HE would involve adjustments in the model's architecture to accommodate the additional computation and latency overheads introduced by HE.

3. Decentralized Computation Nodes

Later on, given Hypercycle's decentralized nature, computation can be distributed across multiple nodes. Each node could handle different parts of the LLM computation or different user requests simultaneously. This setup not only aids in managing the computational load but also reinforces data privacy by dispersing the processing across several points, reducing the risk of data leakage from any single point.

4. Optimization for Performance

Homomorphic encryption is computationally intensive. Therefore, optimization techniques such as batching (processing multiple inputs together) and approximating non-linear functions (like activations in neural networks) to HE-friendly operations are crucial. Additionally, leveraging hardware accelerations such as GPUs or custom ASICs designed for encrypted computations could significantly reduce latency and improve throughput.

5. Security and Compliance Framework

The solution will adhere to stringent security protocols, including regular audits and compliance checks to ensure it meets global data protection standards like GDPR. The use of zero-knowledge proofs alongside HE could further enhance security by allowing verification of computations without revealing any underlying data.

6. Scalability and Ecosystem Integration

Lastly, the scalability of this system can be ensured through adaptive load balancing across the decentralized network and dynamic resource allocation based on demand and computational complexity. Integrating this system within SingularityNet’s AI marketplace could allow for seamless access for developers and businesses, fostering an ecosystem where secure, private AI applications thrive.

The competition and our USPs

Yes

Describe how your solution distinguishes itself from other solutions (if exist) and how it will succeed in the market.

The stringent privacy guarantees provided by our solution are particularly attractive to industries that handle sensitive information and are subject to strict regulatory requirements, such as healthcare, finance, and legal sectors. As regulations like GDPR and HIPAA increasingly influence technology deployment strategies, our solution is well-positioned as a compliant, ready-to-deploy option for businesses in these sectors.

By ensuring that all data processed by the LLM remains encrypted, user trust is significantly enhanced. This trust is crucial for user adoption, as concerns over privacy and data misuse are major barriers to the deployment of AI solutions. Our solution directly addresses these concerns, making it more attractive to privacy-conscious users and organizations.

Our team

Our team members consist of a balanced and pretty diverse group of individuals due to the complexity of the project.

  1. Rojo Kaboti (Project lead): PhD Researcher (Coding theory, Homomorphic encryption, AI), Data Scientist, SingularityNet ambassador. 
  2. Gledis Zeneli: Backend engineer, Low-level programming.
  3. Safaa Anour: Data Scientist, AI engineer.
  4. Rana Meltem: Front-end Engineer
  5. Mohamed Amine Amir: Full-stack engineer.
     
View Team

What we still need besides budget?

No

Existing resources we will leverage for this project

Yes

Description of existing resources

Our project aims to utilize multiple existing and open-source LLMs in our testing and development phases. We also have some of the theoretical work already finalized as part of our academic research which will speed up the work on this project.

Open Source Licensing

gnu

AI services (New or Existing)

ZK-LLM

Type

New AI service

Purpose

The AI service is a Zero KNowledge Large Language Model the service does inference using the ciphertext of the prompt.

AI inputs

Prompt: Encrypted tokens (Homomorphically encrypted). Encryption is done on the client side

AI outputs

Answer: Encrypted tokens (Homomorphically encrypted). Decryption is done on the client side.

Proposal Video

Placeholder for Spotlight Day Pitch-presentations. Video's will be added by the DF team when available.

  • Total Milestones

    6

  • Total Budget

    $125,000 USD

  • Last Updated

    14 May 2024

Milestone 1 - API Calls & Hostings

Description

This milestone represents the required reservation of 25% of your total requested budget for API calls or hosting costs. Because it is required we have prefilled it for you and it cannot be removed or adapted.

Deliverables

You can use this amount for payment of API calls on our platform. Use it to call other services or use it as a marketing instrument to have other parties try out your service. Alternatively you can use it to pay for hosting and computing costs.

Budget

$31,250 USD

Milestone 2 - Initial report

Description

This step includes the initial design specification algorithms specification and technical details. We will also prepare a literature review that is meant to be consumed by the large public. The literature review will inform the articles and educational videos we will make for community members and future clients to be informed about the concept of our solution.

Deliverables

- Initial report. - 2 articles. - 2 educational videos.

Budget

$18,000 USD

Milestone 3 - Client side Encryption

Description

In this milestone we will work on the client-side development of the homomorphic encryption-decryption integrated with the text-2-vec modules.

Deliverables

- Code base (privately shared through GitHub). - Public Jupyter notebook for public tests. - Development report.

Budget

$20,000 USD

Milestone 4 - MVP & Tests (v 0.1)

Description

In this step we aim to present the first public version of our codebase we will also do public tests in the DF town hall and another public meeting to get feedback on the initial product.

Deliverables

- Code base (privately shared through GitHub). - Public Jupyter notebook for public tests. - Development report.

Budget

$25,000 USD

Milestone 5 - SingularityNet integration

Description

In this milestone we aim to integrate our AI service into the platform and conduct multiple tests to ensure the reliability of our service.

Deliverables

- Public link to the service. - Development report.

Budget

$20,000 USD

Milestone 6 - MVP & Tests (v 0.2)

Description

In this step we aim to present the new public version of our codebase we will also do public tests in the DF town hall and another public meeting to get feedback on the initial product.

Deliverables

- Code base (privately shared through GitHub). - Public Jupyter notebook for public tests. - Final report.

Budget

$10,750 USD

Join the Discussion (0)

Reviews & Rating

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2 ratings
  • 1
    user-icon
    Max1524
    May 18, 2024 | 12:28 PM

    Overall

    5

    • Feasibility 5
    • Viability 4
    • Desirabilty 5
    • Usefulness 5
    Carefully focus on technical implementation

    My opinion is that if the team wants to improve and optimize performance, it must be really meticulous in terms of technique, from planning, application to testing and technical evaluation. Only then will the safe ZERO KNOWLEDGE LLM ensure success.

  • 1
    user-icon
    Joseph Gastoni
    May 15, 2024 | 9:14 AM

    Overall

    5

    • Feasibility 5
    • Viability 4
    • Desirabilty 5
    • Usefulness 5
    HE for zero-knowledge secure LLMs on SNet

    Integrating HE for zero-knowledge secure LLMs on SingularityNet has high potential but faces significant technical challenges. Careful planning and execution are required to optimize performance, ensure robust security, and educate users about the benefits of this technology. Focusing on specific use cases and industries with strict data privacy requirements can help drive initial adoption and pave the way for broader application across the AI landscape.

    This project proposes integrating homomorphic encryption (HE) into Large Language Models (LLMs) on SingularityNet for zero-knowledge security. Here's a breakdown of its strengths and weaknesses:

    Feasibility:

    • Challenging: HE is computationally expensive, and integrating it with LLMs requires significant expertise in cryptography and AI.
    • Strengths: The concept leverages existing technologies (HE, LLMs) but requires novel integration and optimization.
    • Weaknesses: Developing a performant and secure system with HE for LLMs is a complex technical challenge.

    Viability:

    • Moderate: Success depends on overcoming technical hurdles, user adoption, and potential regulatory landscape changes.
    • Strengths: The project addresses a growing need for privacy-preserving AI, especially in sensitive sectors.
    • Weaknesses: The computational overhead of HE may limit performance and user adoption. Regulatory frameworks for HE-based AI are still evolving.

    Desirability:

    • High: Privacy-preserving AI with zero-knowledge security is highly desirable for industries with strict data regulations.
    • Strengths: The project focuses on a critical need for secure AI and aligns with SingularityNet's decentralized approach.
    • Weaknesses: Educating users on the benefits of HE and potential performance trade-offs compared to traditional LLMs is important.

    Usefulness:

    • High: The project has the potential to significantly improve data security and trust in AI applications across various sectors.
    • Strengths: Enabling AI to process sensitive data without decryption opens doors for broader AI adoption in healthcare, finance, and legal domains.
    • Weaknesses: The long-term impact on user adoption and real-world applications depends on overcoming performance limitations and user education.

    Besides, the project should consider:

    • Focusing on specific use cases and industries where data privacy is paramount is crucial for initial adoption.
    • Demonstrating the performance improvements achieved through optimization techniques (batching, hardware acceleration) is important.
    • Developing clear user interfaces and documentation explaining the security benefits of HE can build trust with potential users.

    Here are some strengths of this project:

    • Addresses a critical need for privacy-preserving AI in sectors with strict data regulations.
    • Leverages existing technologies (HE, LLMs) and integrates them into SingularityNet's decentralized platform.
    • Offers a unique solution for secure AI with zero-knowledge security, enhancing user trust and compliance.

    Here are some challenges to address:

    • The technical complexity of integrating HE with LLMs while maintaining performance and scalability.
    • Educating users on the benefits of HE and potential performance trade-offs compared to traditional LLMs.
    • Evolving regulatory landscape regarding AI and HE-based solutions.

     

Summary

Overall Community

5

from 2 reviews
  • 5
    2
  • 4
    0
  • 3
    0
  • 2
    0
  • 1
    0

Feasibility

5

from 2 reviews

Viability

4

from 2 reviews

Desirabilty

5

from 2 reviews

Usefulness

5

from 2 reviews