Efficient Privacy-Preserving Similarity Range Query With Quadsector Tree in eHealthcare

Abstract

As a consequence of advance in the Internet of Things (IoT) and big data technology, smart eHealthcare has emerged and greatly enabled patients to enjoy high-quality healthcare services in disease prediction, clinical decision making and healthcare surveillance. Meanwhile, in order to support the dramatic increase of healthcare data, healthcare centers often outsource the on-premises data to a powerful cloud and deploy the cloud server to manage the data. However, since the healthcare data usually contain some sensitive information and also the cloud server is not fully trusted, healthcare centers need to encrypt the data before outsourcing them to the cloud. Unfortunately, data encryption inevitably hinders some advanced applications of the data like the similarity range query in cloud. Although many studies on similarity range query over encrypted data have been reported, most of them still have some limitations in security, efficiency and practicality. Aiming at this challenge, in this article, we propose a new efficient privacy-preserving similarity range query (EPSim) scheme. Specifically, we first present a modified asymmetric scalar-product-preserving encryption (ASPE) scheme and prove it is selectively secure. Then, we introduce a Quadsector tree to represent the data, and employ a filtration condition to design an efficient algorithm for efficient similarity range queries over the Quadsector tree. Finally, we propose our EPSim scheme by integrating the modified ASPE scheme and Quadsector tree. Detailed security analysis indicates that our proposed EPSim scheme is really secure. In addition, extensive performance evaluations are conducted, and the results also demonstrate it is efficient and practical.