SDTA: Secure Decentralized Trading Alliance for Electronic Medical Data

Abstract

Abstract Massive medical data are indispensable for training diagnostic models to provide high-quality health monitoring services. The methods for sharing data in existing works involve securely and essentially copying data but often overlook the integration and efficiency of data storage, exchange and application. In this paper, we propose a Secure Decentralized Trading Alliance (SDTA) to encompass the entire process holistically. With monetary incentives, we formulate a chain-net structure for recording data digests and authentic transactions, thereby transforming data sharing into data trading without duplicating data storage. Data privacy is promised by encryption. To manage and employ encrypted medical data, users can update and search their encrypted data using an index and keywords, subsequently retrieving data within the SDTA framework. It is realized by a novel dynamic searchable symmetric encryption (SSE) with an $l$-level access strategy, which confines users to data pertinent solely to them, thus circumventing unnecessary data leakage. We scrutinize the storage efficiency and prove the fairness and security of SDTA. Finally, we generate datasets of varying sizes, where the time required to search for a single keyword is approximately 0.04 s with 1 000 000 (keyword, identifier) pairs, showing it quite acceptable.