Towards ECDSA key derivation from deep embeddings for novel Blockchain applications

TL;DR

This paper introduces a method to generate ECDSA keys from deep learning embeddings, enabling blockchain transactions directly from high-quality learned representations across various data domains.

Contribution

It presents a novel approach to derive cryptographic keys from deep embeddings, bridging deep learning and blockchain technology for new applications.

Findings

ECDSA keys can be derived from deep embeddings

Blockchain addresses can be generated from these keys

Enables transfer of funds/data using deep learning representations

Abstract

In this work, we propose a straightforward method to derive Elliptic Curve Digital Signature Algorithm (ECDSA) key pairs from embeddings created using Deep Learning and Metric Learning approaches. We also show that these keys allows the derivation of cryptocurrencies (such as Bitcoin) addresses that can be used to transfer and receive funds, allowing novel Blockchain-based applications that can be used to transfer funds or data directly to domains such as image, text, sound or any other domain where Deep Learning can extract high-quality embeddings; providing thus a novel integration between the properties of the Blockchain-based technologies such as trust minimization and decentralization together with the high-quality learned representations from Deep Learning techniques.