Secure semantic search using deep learning in a blockchain-assisted multi-user setting

Abstract Deep learning-based semantic search (DLSS) aims to bridge the gap between experts and non-experts in search. Experts can create precise queries due to their prior knowledge, while non-experts struggle with specific terms and concepts, making their queries less precise. Cloud infrastructure offers a practical and scalable platform for data owners to upload their data, making it accessible to intended data users. However, the contemporary single-owner/single-user (S/S) approach to DLSS schemes falls short of effectively leveraging the inherent multi-user capabilities of cloud infrastructure. Furthermore, most of these schemes delegate the dissemination of secret keys to a single trust point within the mutual distrust scenario in cloud infrastructure. This paper proposes a Secure Semantic Search using Deep Learning in a Blockchain-Assisted Multi-User Setting $$(S^3DBMS)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>S</mml:mi> <mml:mn>3</mml:mn> </mml:msup> <mml:mi>D</mml:mi> <mml:mi>B</mml:mi> <mml:mi>M</mml:mi> <mml:mi>S</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> . Specifically, the seamless integration of attribute-based encryption with transfer learning allows the construction of DLSS in multi-owner/multi-user (M/M) settings. Further, blockchain’s smart contract mechanism allows a multi-attribute authority consensus-based generation of user private keys and system-wide global parameters in a mutual distrust M/M scenario. Finally, our scheme achieves privacy requirements and offers improved security and accuracy.