Publication Type

Journal Article

Version

acceptedVersion

Publication Date

7-2020

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.

Keywords

Medical services, Encryption, Privacy, Access control, Data privacy, Internet of Things, IoT, policy hiding, privacy aware, smart health

Discipline

Health Information Technology | Information Security

Research Areas

Cybersecurity

Publication

IEEE Internet of Things

Volume

7

Issue

7

First Page

6566

Last Page

6575

ISSN

2327-4662

Identifier

10.1109/JIOT.2020.2974257

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/JIOT.2020.2974257

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