scholarly journals Lightweight Privacy-Preserving Data Sharing Scheme for Internet of Medical Things

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhuo Zhao ◽  
Chingfang Hsu ◽  
Lein Harn ◽  
Qing Yang ◽  
Lulu Ke

Internet of Medical Things (IoMT) is a kind of Internet of Things (IoT) that includes patients and medical sensors. Patients can share real-time medical data collected in IoMT with medical professionals. This enables medical professionals to provide patients with efficient medical services. Due to the high efficiency of cloud computing, patients prefer to share gathering medical information using cloud servers. However, sharing medical data on the cloud server will cause security issues, because these data involve the privacy of patients. Although recently many researchers have designed data sharing schemes in medical domain for security purpose, most of them cannot guarantee the anonymity of patients and provide access control for shared health data, and further, they are not lightweight enough for IoMT. Due to these security and efficiency issues, a novel lightweight privacy-preserving data sharing scheme is constructed in this paper for IoMT. This scheme can achieve the anonymity of patients and access control of shared medical data. At the same time, it satisfies all described security features. In addition, this scheme can achieve lightweight computations by using elliptic curve cryptography (ECC), XOR operations, and hash function. Furthermore, performance evaluation demonstrates that the proposed scheme takes less computation cost through comparison with similar solutions. Therefore, it is fairly an attractive solution for efficient and secure data sharing in IoMT.

2021 ◽  
Vol 58 (4) ◽  
pp. 102604
Author(s):  
Renpeng Zou ◽  
Xixiang Lv ◽  
Jingsong Zhao

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Jiawei Zhang ◽  
Ning Lu ◽  
Teng Li ◽  
Jianfeng Ma

Mobile cloud computing (MCC) is embracing rapid development these days and able to provide data outsourcing and sharing services for cloud users with pervasively smart mobile devices. Although these services bring various conveniences, many security concerns such as illegally access and user privacy leakage are inflicted. Aiming to protect the security of cloud data sharing against unauthorized accesses, many studies have been conducted for fine-grained access control using ciphertext-policy attribute-based encryption (CP-ABE). However, a practical and secure data sharing scheme that simultaneously supports fine-grained access control, large university, key escrow free, and privacy protection in MCC with expressive access policy, high efficiency, verifiability, and exculpability on resource-limited mobile devices has not been fully explored yet. Therefore, we investigate the challenge and propose an Efficient and Multiauthority Large Universe Policy-Hiding Data Sharing (EMA-LUPHDS) scheme. In this scheme, we employ fully hidden policy to preserve the user privacy in access policy. To adapt to large scale and distributed MCC environment, we optimize multiauthority CP-ABE to be compatible with large attribute universe. Meanwhile, for the efficiency purpose, online/offline and verifiable outsourced decryption techniques with exculpability are leveraged in our scheme. In the end, we demonstrate the flexibility and high efficiency of our proposal for data sharing in MCC by extensive performance evaluation.


IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 28019-28027 ◽  
Author(s):  
Dong Zheng ◽  
Axin Wu ◽  
Yinghui Zhang ◽  
Qinglan Zhao

Author(s):  
Yu Niu ◽  
Ji-Jiang Yang ◽  
Qing Wang

With the pervasive using of Electronic Medical Records (EMR) and telemedicine technologies, more and more digital healthcare data are accumulated from multiple sources. As healthcare data is valuable for both commercial and scientific research, the demand of sharing healthcare data has been growing rapidly. Nevertheless, health care data normally contains a large amount of personal information, and sharing them directly would bring huge threaten to the patient privacy. This paper proposes a privacy preserving framework for medical data sharing with the view of practical application. The framework focuses on three key issues of privacy protection during the data sharing, which are privacy definition/detection, privacy policy management, and privacy preserving data publishing. A case study for Chinese Electronic Medical Record (ERM) publishing with privacy preserving is implemented based on the proposed framework. Specific Chinese free text EMR segmentation, Protected Health Information (PHI) extraction, and K-anonymity PHI anonymous algorithms are proposed in each component. The real-life data from hospitals are used to evaluate the performance of the proposed framework and system.


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