Building an End-to-End Encrypted Shazam with Homomorphic Encryption
A tutorial on how to code a privacy-preserving version of Shazam using Zama Concrete ML.
Building an End-to-End Encrypted Shazam with Homomorphic Encryption launched on February 21, 2024 with 59 votes, #898 of 3,962 launches that month and more than 90% of that year's launches. The site was still online in September 2026, 2 years on.
In plain words
This project demonstrates how to build a music recognition service similar to Shazam using end-to-end encryption and homomorphic encryption technology. It is for developers interested in privacy-preserving applications and cryptographic techniques. The standout feature is the ability to identify songs while keeping user queries and the music database encrypted throughout the entire process, preventing any party from accessing the underlying data.
written from the facts on this page · September 2026
From the sources
Shazam's algorithm, detailed in a 2003 paper , involves users sending queries to its servers for matching in its central database—a prime scenario for applying FHE to protect user data. We challenged bounty participants to create a Shazam-esque app using FHE, allowing for any machine learning technique or the method outlined in Shazam's original paper. The solution is based on a custom implementation using machine learning models, and matches a song against a 1000-song database in less than half a second, while providing a client/server application based on the Concrete ML classifiers. Spectrograms are a popular approach to extracting features from raw audio. Spectrograms are created by…from zama.ai
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