Verifiable keyword search for secure big data-based mobile healthcare networks with fine-grained authorization control

Mobile healthcare networks (MHNs) are increasingly viewed as potential applications for further improving the quality and efficiency of healthcare, with the rapid development of wearable devices. Wearable devices can generate a huge amount of health data, causing big data to be one of the most prominent features. The privacy and security of big data-based MHNs are major concerns of users, and these are the overriding obstacles that stand in the way of the wider adoption of MHNs. For the sake of security, health data is encrypted and stored on an untrusted server. However, the flexibility of the data is thereby affected, such as a search over encrypted health data, or authorization control for a search and verification of the search result. To address these problems, we propose a verifiable keyword search scheme for big data-based MHNs with fine-grained authorization control. In the proposed scheme, when sending the search request to the healthcare provider for the first time, the user needs to check whether he or she has the right to search within encrypted health data. Only authorized users can generate valid trapdoors for searching. Our verification technique is constructed based on an invertible Bloom lookup table and a Merkle hash tree, which can verify the completeness and correctness of the search result even if an empty set is returned by a dishonest healthcare provider. The security analysis shows that the proposed scheme is secure against chosen keyword attacks. The proposed scheme is efficient with low computation load, which can be used to perform eyword searches and verify the search results quickly in a big data environment.