A Review of Open-Source Fully Homomorphic Encryption Libraries: Zama.ai Concrete Compiler, Applications and Vulnerability

Abstract

Fully Homomorphic Encryption (FHE) is an advanced cryptographic technique that enables computational operations to be
performed on encrypted data without the need for decryption. In other words, FHE allows operations to be conducted
directly on ciphertexts, producing encrypted results that, when decrypted, correspond to the output of the operations
performed on the plaintext data. This revolutionary capability ensures data privacy and security throughout the entire computation process, as the data remains encrypted at all times, even during computation. FHE schemes typically involve
complex mathematical operations and algorithms, often based on lattice-based cryptography or other mathematical structures, to enable secure and efficient computation on encrypted data. Substantial progress has been achieved in the realm of FHE and its application since 2015, yielding enhanced efficacy, heightened security, and augmented feasibility. This review paper discusses and reviews diverse FHE schemes/libraries, and the extent of progress attained hitherto and how the possibilities of adoption of the scheme in industry is being propagated, using research questions as a guide, we endeavor to utilize searches across various academic databases and industry repositories for peer-reviewed papers, articles, and books. While some of the examined papers suggested new techniques to improve the security of transferred data, several of the publications provided novel schemes for FHE to maximize efficiency and minimize noise. Special emphasis is placed on the open-source tools and libraries implementing FHE scheme, notably Concrete (developed using TFHE Scheme), an innovation by Zama.ai, a preeminent research establishment specializing in FHE research and development. Since writing FHE programs can be difficult, Concrete, based on LLVM, makes this process easier for developers with the ability to compile Python functions (that may include NumPy) to their FHE equivalents, to operate on encrypted data. The applications of the library are examined, encompassing accomplishments, limitations, and vulnerabilities. Conclusively, prospective avenues for advancement are underscored, deliberated upon, and illuminated.