Stories and Reviews
April 17, 2024

Exploring FHE Adoption through the FHERMA Challenge Platform

In Brief

FHERMA stands out from other solutions with a range of distinctive features. Initially, it provides rapid evaluation of solutions, delivering results within seconds, contrasting with the extended verification procedures observed in conventional bounty programs. The platform ensures transparency and fairness by utilizing fully automated evaluation processes, thus eliminating any potential bias introduced by human involvement. Additionally, participants can track their real-time rankings on a dynamic leaderboard, facilitating continuous improvement of solutions as needed.

There’s much talk about how Fully Homomorphic Encryption (FHE) technology could transform privacy in blockchain systems. It’s seen as a way to tackle privacy issues by allowing computations on encrypted data. However, despite advances in its development, Fully Homomorphic Encryption continues to face challenges in achieving broad adoption, primarily due to performance issues and a shortage of development ecosystem. Moreover, integrating FHE into existing blockchain setups poses compatibility and scalability challenges.

Fair Math has responded to these challenges by introducing FHERMA, a dedicated platform designed for hosting Fully Homomorphic Encryption challenges. Developed collaboratively by Fair Math and OpenFHE teams, FHERMA is committed to facilitating the establishment of an extensive open-source library of FHE components. This initiative aims to simplify application development and accelerate FHE adoption, particularly focusing on Machine Learning and Blockchain applications.

The Power of Black Box Challenges on FHERMA

An interesting aspect of Fully Homomorphic Encryption (FHE) lies in its capacity to enable one participant, the prover, to demonstrate to another participant, the verifier, their ability to solve a specific problem using an algorithm without revealing any details about the algorithm itself. In this process, the prover manipulates encrypted test data provided by the verifier and presents the processed ciphertext as evidence. The verifier then decrypts and compares it with the expected output. Particularly in scenarios where each ciphertext represents a batch of numerous elements, the likelihood of randomly guessing the result diminishes significantly.

This concept forms the foundation of the Black Box challenges hosted on the FHERMA platform. These challenges play a crucial role in Privacy-Preserving Machine Learning, meeting the needs of dataset owners who require an ML model trained on their proprietary data while keeping it undisclosed. Homomorphic encryption emerges as a fitting solution for such requirements, with FHERMA’s Black Box challenges serving as a platform to involve diverse developers in crafting the necessary model.

Fostering Collaboration and Innovation in FHE

“Throughout the development of FHERMA,” said Gurgen Arakelov, the founder of Fair Math,  “a primary goal has been to foster a reliable ecosystem that brings together a diverse range of researchers, including mathematicians proficient in FHE theory, as well as cryptographers, machine learning specialists, and others. This fosters equitable and transparent competition among diverse experts while also facilitating their integration into the open-source community.”

Participants contribute algorithms and components, subjecting them to rigorous testing within the platform. Notably, FHERMA’s Black Box challenges allow participants to assess solution without revealing the source code. The platform assesses submissions based on the accuracy of the cipher provided by participants, promoting continuous enhancement through interactive leaderboards.

In addition to accuracy, performance evaluation becomes crucial in certain scenarios, leading to the introduction of White Box Challenges. For example, the ongoing CIFAR-10 Image Classification challenge requires participants to develop machine learning models that can effectively classify encrypted images from the CIFAR-10 dataset. Submissions are evaluated for both performance and accuracy, ensuring the best solutions while preserving confidentiality.

FHE Challenges: A Fair and Open Source Perspective

FHERMA stands out from other solutions with a range of distinctive features. Initially, it provides rapid evaluation of solutions, delivering results within seconds, contrasting with the extended verification procedures observed in conventional bounty programs. The platform ensures transparency and fairness by utilizing fully automated evaluation processes, thus eliminating any potential bias introduced by human involvement. Additionally, participants can track their real-time rankings on a dynamic leaderboard, facilitating continuous improvement of solutions as needed.

Moreover, FHERMA provides the flexibility to create challenges based on various encryption schemes, with new challenges introduced monthly. The platform actively collaborates with leading FHE researchers such as OpenFHE, Lattigo, and IBM Research to identify and define valuable topics for the FHE community. Its adaptable architecture is designed to accommodate diverse usage scenarios and task specifications requiring homomorphic encryption.

An integral aspect of the platform is its commitment to community, openness, and fairness. Unlike other platforms that may impose restrictive agreements on participants, FHERMA only requires solutions to be distributed under the Apache 2.0 license, fostering an environment of collaboration and knowledge-sharing within the FHE community.

Disclaimer

In line with the Trust Project guidelines, please note that the information provided on this page is not intended to be and should not be interpreted as legal, tax, investment, financial, or any other form of advice. It is important to only invest what you can afford to lose and to seek independent financial advice if you have any doubts. For further information, we suggest referring to the terms and conditions as well as the help and support pages provided by the issuer or advertiser. MetaversePost is committed to accurate, unbiased reporting, but market conditions are subject to change without notice.

About The Author

Gregory, a digital nomad hailing from Poland, is not only a financial analyst but also a valuable contributor to various online magazines. With a wealth of experience in the financial industry, his insights and expertise have earned him recognition in numerous publications. Utilising his spare time effectively, Gregory is currently dedicated to writing a book about cryptocurrency and blockchain.

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Gregory Pudovsky
Gregory Pudovsky

Gregory, a digital nomad hailing from Poland, is not only a financial analyst but also a valuable contributor to various online magazines. With a wealth of experience in the financial industry, his insights and expertise have earned him recognition in numerous publications. Utilising his spare time effectively, Gregory is currently dedicated to writing a book about cryptocurrency and blockchain.

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