MPKS: Efficient and Privacy-Enhanced Multi-Party Keyword-Oriented Similarity Query in eHealthcare

Cloud computing has accelerated the growth of data outsourcing query services. In this paper, a privacy-enhanced multi-party keyword-oriented similarity query (MPKS) scheme is proposed. The scheme can support the flexible scalability of multiple data sources, which makes up for the problem of insufficient research on multi-party joint queries in existing study. In addition, the scheme avoids the path pattern privacy issues that are naturally inherent in tree structures while leveraging them to speed up queries. Specifically, to support the secure and flexible scalability of data sources, a multi-key symmetric homomorphic encryption (MSHE) scheme is designed. To ensure query efficiency in multi-party environments, the scalable system model involving multiple data sources is reconstructed, and efficient parallel queries in the multi-party environment are achieved by explicitly delineating the responsibilities of each server. Under the new system model, to preserve data privacy and path pattern privacy, a privacy-preserving filtration operation (PFO) and a privacy-preserving verification operation (PVO) are designed based on MSHE, ensuring that only honest parties can obtain the filtration information on tree nodes. Extra encryption flags are added to the tree nodes and the node sequence is obfuscated to confuse the view of other servers. The aforementioned two designs constitute an oblivious tree-based traversal method. We formally prove the security of the MPKS scheme under a simulation-based real/ideal world, and the security of the MSHE protocol. Finally, comprehensive experiments demonstrate the efficiency and practicality of the proposed MPKS, which is about two orders of magnitude faster than the conventional scheme in the dataset outsourcing phase and four orders of magnitude more optimized in the query processing phase.