Towards ECDSA key derivation from deep embeddings for novel Blockchain applications
This enables novel blockchain applications by integrating deep learning representations with blockchain's decentralized properties, though it appears incremental as it applies existing methods to new data.
The authors developed a method to generate ECDSA cryptographic key pairs from deep learning embeddings, enabling the creation of cryptocurrency addresses for transferring funds or data across domains like images, text, and sound.
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.