Torus-based Fully Homomorphic Encryption Enhancement with Genetic Algorithm
Budget: $250 – $750 USD
Job Title:
Cryptography Developer (TFHE + Genetic Algorithm Optimization)
Project Title:
“Optimizing Torus Fully Homomorphic Encryption (TFHE) using Genetic Algorithm (TFHE+GA): An Indirect Approach”
Project Overview:
We seeking a skilled and research-oriented developer to assist in the simulation, implementation and optimization of TFHE (Torus Fully Homomorphic Encryption) using Genetic Algorithm (GA) strategies. This role is part of a doctoral research project aimed at significantly improving bootstrapping performance, ciphertext size, and key sizes without compromising cryptographic security.
Key Objectives:
The developer will help achieve the following specific goals:
a. Develop and evaluate GA-based strategies for optimizing the bootstrapping process in TFHE, targeting a 20% reduction in bootstrapping time (from 13 ms to ~10.4 ms).
b. Implement GA-based techniques to minimize the number of bootstrapping operations for evaluating complex Boolean functions in TFHE, with a target reduction of 15%.
c. Optimize the trade-off between cleartext bit-size and the number of homomorphic additions before bootstrapping, aiming for a 10% reduction in ciphertext size while maintaining cryptographic security.
d. Design and evaluate GA-based methods for reducing key and ciphertext sizes in TFHE, targeting a 50% reduction in key size without sacrificing the security of current NTRU-based encryption schemes.
Responsibilities:
Simulate, Implement and modify TFHE libraries in C++ or Python (preferably TFHE, Concrete-ML, or similar)
Design and integrate Genetic Algorithm frameworks using Python (e.g., DEAP, PyGAD, or custom-built solutions)
Benchmark and test cryptographic performance (time, size, and security level)
Document code, experiments, and optimizations for inclusion in research publications and dissertation materials
Work collaboratively with the principal researcher and provide regular progress updates
Required Skills & Qualifications:
Strong experience with cryptographic programming (especially homomorphic encryption, ideally TFHE)
Proficiency in Genetic Algorithms and optimization techniques
Proficient in C++ and Python (especially libraries for crypto and evolutionary computation)
Familiarity with performance profiling, optimization, and benchmarking tools
Background in computer science, mathematics, cryptography, or related field (graduate degree preferred, but not required)
Preferred Qualifications:
Previous experience with FHE libraries (TFHE, Concrete, SEAL, PALISADE, etc.)
Understanding of noise management, bootstrapping, and ciphertext parameter tuning
Familiarity with NTRU and lattice-based cryptographic schemes
Published work or demonstrable portfolio related to crypto/GA/AI optimization
Project Duration:
3 days for the GA simulation (urgent) and another days for TFHE (with possible extension based on performance and results)
Compensation:
Negotiable, based on experience and contribution level
Work Arrangement:
Remote
Cryptography Developer (TFHE + Genetic Algorithm Optimization)
Project Title:
“Optimizing Torus Fully Homomorphic Encryption (TFHE) using Genetic Algorithm (TFHE+GA): An Indirect Approach”
Project Overview:
We seeking a skilled and research-oriented developer to assist in the simulation, implementation and optimization of TFHE (Torus Fully Homomorphic Encryption) using Genetic Algorithm (GA) strategies. This role is part of a doctoral research project aimed at significantly improving bootstrapping performance, ciphertext size, and key sizes without compromising cryptographic security.
Key Objectives:
The developer will help achieve the following specific goals:
a. Develop and evaluate GA-based strategies for optimizing the bootstrapping process in TFHE, targeting a 20% reduction in bootstrapping time (from 13 ms to ~10.4 ms).
b. Implement GA-based techniques to minimize the number of bootstrapping operations for evaluating complex Boolean functions in TFHE, with a target reduction of 15%.
c. Optimize the trade-off between cleartext bit-size and the number of homomorphic additions before bootstrapping, aiming for a 10% reduction in ciphertext size while maintaining cryptographic security.
d. Design and evaluate GA-based methods for reducing key and ciphertext sizes in TFHE, targeting a 50% reduction in key size without sacrificing the security of current NTRU-based encryption schemes.
Responsibilities:
Simulate, Implement and modify TFHE libraries in C++ or Python (preferably TFHE, Concrete-ML, or similar)
Design and integrate Genetic Algorithm frameworks using Python (e.g., DEAP, PyGAD, or custom-built solutions)
Benchmark and test cryptographic performance (time, size, and security level)
Document code, experiments, and optimizations for inclusion in research publications and dissertation materials
Work collaboratively with the principal researcher and provide regular progress updates
Required Skills & Qualifications:
Strong experience with cryptographic programming (especially homomorphic encryption, ideally TFHE)
Proficiency in Genetic Algorithms and optimization techniques
Proficient in C++ and Python (especially libraries for crypto and evolutionary computation)
Familiarity with performance profiling, optimization, and benchmarking tools
Background in computer science, mathematics, cryptography, or related field (graduate degree preferred, but not required)
Preferred Qualifications:
Previous experience with FHE libraries (TFHE, Concrete, SEAL, PALISADE, etc.)
Understanding of noise management, bootstrapping, and ciphertext parameter tuning
Familiarity with NTRU and lattice-based cryptographic schemes
Published work or demonstrable portfolio related to crypto/GA/AI optimization
Project Duration:
3 days for the GA simulation (urgent) and another days for TFHE (with possible extension based on performance and results)
Compensation:
Negotiable, based on experience and contribution level
Work Arrangement:
Remote