A Privacy-Preserving Hybrid Range Search Scheme Over Encrypted Electronic Medical Data in IoT Systems

Abstract

Electronic wearable devices play an important role in the Internet of Things (IoT) systems for collecting medical data. Searching over such numerical medical data help to provide better service and treatment. However, user and data security is a major barrier to public adoption. Existing approaches designed to facilitate secure (range) searches over encrypted data generally incur expensive computational overhead, suffer from unexpected information leakage, and/or have high false-positive results. Therefore, in this paper, we design a hybrid searchable encryption scheme that supports efficient, secure, and accurate range searches over encrypted data sensed and collected from medical IoT devices. The designed graph structure helps to filter out most of the false data, and the batching processing on ciphertexts accelerates the removal of irrelevant data. Unlike most prior works, the proposed random index hides the distribution of data, and the probabilistic fixed-length trapdoor hides the range size and repetition of the query. If necessary, all the encrypted data can be refreshed by the cloud server after a range search. The scheme is proven to be secure in a simulation-based model. Then, we evaluate the performance of our proposed scheme on Microsoft Azure cloud servers and Azure IoT Central. The comparisons with several prior works demonstrate that our scheme supports more efficient secure range searches.