This GitHub page contains projects related to my research, as well as some older projects that I have archived for reference.

I am currently focused full-time on the development of PyACES, a fully homomorphic encryption (FHE) scheme. In the future, I plan to use this scheme for implementing secure machine learning models, particularly in natural language processing (NLP).

Alongside this, I am developing Intuitionistic Language Models (ILM), a toolkit for language models based on hierarchical tokenization. ILM offers a structured alternative to standard tokenization by organizing tokens into multiple levels.

As these projects evolve, I plan to join GitHub Sponsors to allow those who find value in this work to support its continued development. If you're interested in advancing research in homomorphic encryption, structured language models, or privacy-preserving machine learning, your future support, whether through sponsorships, contributions, or discussions, would be greatly appreciated! 🚀

This repository contains the Python implementation of ACES, a fully homomorphic encryption scheme introduced in the research paper "Constructing a Fully Homomorphic Encryption Scheme with the Yoneda Lemma".

I am actively working on a developer guide for the new codebase (see the dev branch).

Looking ahead, I aim to introduce the following enhancements (subject to funding or sponsorship):

• 📁 Develop a function for generating cryptographic data with an efficient locator-director database (RAM).

• 💾 Enable local storage of cryptographic data (ROM) and implement secure key management.

• 🔒 Implement file encryption (ROM).

• 🧮 Support complex data types (Int8, Int16, Int32, String, Bool) and their arithmetic operations.

• ⚡ Optimize for CPU & GPU performance:

  • ⚙️ Utilize C, C++, or ctypes for efficient polynomial and ciphertext operations.

  • 🎮 Implement GPU acceleration using CUDA.

Intuitionistic Language Models (ILM) is a toolkit for structured language models using hierarchical tokenization. Instead of large predefined vocabularies, ILM organizes tokens into multiple levels, capturing both local and global patterns.

Inspired by biological encoding principles, ILM represents language with a structured set of 64 base tokens, akin to DNA's 64 codons. By reducing reliance on memorized mappings and emphasizing hierarchy, ILM provides a foundation for more interpretable and efficient language models.

• Develop a hierarchical tokenizer that structures words into multi-level representations.

• Provide a lightweight and flexible alternative to existing tokenization libraries.

• Explore applications in language model training and structured learning.

• Expand ILM into a complete language model toolkit with tools for training, evaluating, and deploying models.

• Refine the hierarchical tokenization approach to improve efficiency and interpretability.

• Investigate theoretical foundations and potential applications in structured NLP models.