KMSQ: Efficient and Privacy-Preserving Keyword-Oriented Multidimensional Similarity Query in eHealthcare

Extensive research has been conducted on efficient and privacy-preserving similarity queries in eHealthcare, aiming at disease diagnosis based on similar patients while protecting the outsourced sensitive healthcare data. In this article, a new secure similarity query scheme named keyword-oriented multidimensional similarity query (KMSQ) is proposed for eHealthcare. Different from the state-of-the-art similar works, our proposed scheme enables users to query historical similar patients’ records based on their multidimensional physiological characteristics and symptom keywords (two data types) at the same time. Although the query can be securely performed sequentially by formerly proposed schemes, we carefully tailor a binary-decision-PB (BD-PB) tree to index the two data types simultaneously for efficient queries. Furthermore, inspired by the Hilbert exclusion condition and the properties of the polynomial function, an efficient query algorithm based on the BD-PB tree is designed in a filtration–verification manner, which further greatly improves the computational efficiency of queries, especially on the server side. To ensure secure query on untrusted clouds, the BD-PB tree-based KMSQ is protected through multiple encryption techniques. Specifically, function-hiding inner product preserving encryption (FHIPPE) is modified and combined with a lightweight matrix encryption technique to achieve secure data filtration. In addition, a symmetric homomorphic encryption (SHE) scheme is utilized to ensure secure verification that each candidate record in the filtration result satisfies the query requirements. Security analysis demonstrates the modified FHIPPE (MFHIPPE) and our proposed scheme meet the necessary security properties under the honest-but-curious model. Finally, extensive experiments are also conducted to show that KMSQ is computationally efficient.