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Lightweight and Privacy-Aware Fine-Grained Access Control for IoT-Oriented Smart Health - 2020

Research Area:  Internet of Things

Abstract:

With the booming of Internet of Things (IoT), smart health (s-health) is becoming an emerging and attractive paradigm. It can provide an accurate prediction of various diseases and improve the quality of healthcare. Nevertheless, data security and user privacy concerns still remain issues to be addressed. As a high potential and prospective solution to secure IoT-oriented s-health applications, ciphertext policy attribute-based encryption (CP-ABE) schemes raise challenges, such as heavy overhead and attribute privacy of the end users. To resolve these drawbacks, an optimized vector transformation approach is first proposed to efficiently transform the access policy and user attribute set into respective vectors of shorter length while other approaches result in redundant and longer vectors. Our transformation approach can greatly relieve the costly overheard of key generation, encryption, and decryption phases. Then, based on the transformation approach and the offline/online computation technology, we propose a lightweight policy-hiding CP-ABE scheme for the IoT-oriented s-health application. With our proposed scheme, data users in the s-health system can perform lightweight encryption and decryption without leaking any sensitive privacy about the attributes of the user. Finally, the formal security analysis, the theoretic performance evaluation and experiment results indicate that the solution is secure and efficient.

Author(s) Name:  Jianfei Sun; Hu Xiong; Ximeng Liu; Yinghui Zhang; Xuyun Nie; Robert H. Deng

Journal name:  IEEE Internet of Things Journal

Conferrence name:  

Publisher name:  IEEE

DOI:  DOI: 10.1109/JIOT.2020.2974257

Volume Information:   Volume: 7, Issue: 7, Page(s): 6566 - 6575