February 2024 Summaries

The Zama Bounty Program Season 4 challenged participants to create an onchain game using Zama's fhEVM to maintain private states, resulting in various innovative entries. The successful project "FHEordle" by kroist, an adaptation of the popular Wordle game, stood out for its use of Fully Homomorphic Encryption (FHE) to ensure data privacy on the blockchain, where data is typically public. In this version, players guess a five-letter word represented by encrypted integers within a blockchain setting, using a 26-bit mask to provide feedback on letter presence without revealing specific positions. This game cleverly avoids traditional loops, thereby enhancing efficiency and security. It also employs a Merkle tree to validate words against a list of approved English words, ensuring only the root hash is stored on the blockchain. FHEordle exemplifies the potential of cryptographic techniques in gaming, showing how FHE can elevate privacy and security standards in web3 applications.

Concrete is an open-source fully homomorphic encryption (FHE) compiler designed to simplify the development of FHE programs, incorporating a TFHE Compiler based on LLVM for ease of use. In a tutorial by Zama team member Alexandre Pere, developers are guided on compiling composable functions using the Concrete framework. The tutorial encourages engagement through various channels, including starring the Concrete GitHub repository, reviewing its documentation, participating in community support channels, and contributing to the advancement of FHE through the Zama Bounty and Grant Programs.

Zama's Bounty Program, now in its fifth season, challenges developers to integrate Fully Homomorphic Encryption (FHE) into mainstream applications like Shazam, enhancing privacy without compromising user experience. In Season 4, participants were tasked with creating a privacy-preserving version of Shazam, resulting in two innovative solutions. The first-place winner used machine learning models to match songs in under half a second, leveraging Concrete ML classifiers, while the second-place solution adapted Shazam's original algorithm using Zama's Concrete compiler to generate encrypted song hashes. Both approaches utilized spectrograms and innovative cryptographic techniques to maintain high accuracy rates and demonstrate FHE's potential in privacy protection. The program underscores Zama's commitment to empowering developers to safeguard user privacy through open-source tools and rewards.

The Zama Bounty Program Season 4 posed a challenge to create an encrypted version of Shazam using Fully Homomorphic Encryption (FHE), successfully completed by GitHub user Iamayushanand. The solution involved a two-step process: extracting song signatures on the client side and matching these encrypted signatures against a server-side music database. The approach utilized machine learning models, specifically Concrete ML classifiers, to achieve song recognition with 97% accuracy in under half a second. The method involved generating spectrograms and Mel-frequency cepstral coefficients (MFCC) from audio segments, constructing a song descriptor from these features, and training a multi-class logistic regression model. The model demonstrated impressive performance by maintaining accuracy on both encrypted and cleartext data, highlighting FHE's potential for enhancing privacy in applications. The solution was benchmarked against a scikit-learn implementation, showing comparable results, and underscored the viability of FHE for privacy-centric applications.

Last season, the Zama Bounty Program awarded €45,000 to the developer community for innovative solutions using Fully Homomorphic Encryption (FHE), advancing both FHE applications and enhancing Zama's libraries. Notable achievements included creating a string library for encrypted data, a privacy-preserving version of Shazam, and an on-chain game with private states using Zama's fhEVM programmable privacy. As FHE becomes increasingly practical and accessible, Zama aims to empower developers with open-source libraries for end-to-end encryption, reducing the need for extensive cryptographic expertise. To further support this mission, Zama has launched the Grant Program to fund projects building real-world applications with FHE, such as Concrete Biopython, VaultChem, and GacsBiometrics. The initiative seeks to enable developers to implement privacy features that protect user data, fostering a community engaged in advancing FHE technology.

Concrete ML is a suite of privacy-preserving machine learning tools designed to simplify the integration of Fully Homomorphic Encryption (FHE) for developers, enabling the automatic conversion of machine learning models into their homomorphic equivalents. In a video tutorial, Luis Montero, a machine learning engineer at Zama, demonstrates Concrete ML's latest feature, FHE training. The initiative encourages users to engage with the project by starring the Concrete ML GitHub repository, exploring its documentation, seeking support through community channels, and participating in the Zama Bounty Program & Grant Program to further advance the FHE space.