Efficient and Privacy-preserving Fog-assisted Health Data Sharing Scheme

被引:35
作者
Tang, Wenjuan [1 ]
Ren, Ju [1 ]
Zhang, Kuan [2 ]
Zhang, Deyu [1 ]
Zhang, Yaoxue [1 ]
Shen, Xuemin [3 ]
机构
[1] Cent S Univ, Sch Compmuter Sci & Engn, Changsha 410083, Peoples R China
[2] Univ Nebraska, Dept Elect & Comp Engn, Lincoln, NE 68588 USA
[3] Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON N2L 3G1, Canada
基金
对外科技合作项目(国际科技项目); 中国国家自然科学基金;
关键词
Fog computing; access control; data sharing; e-healthcare; privacy-preservation;
D O I
10.1145/3341104
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Pervasive data collected from e-healthcare devices possess significant medical value through data sharing with professional healthcare service providers. However, health data sharing poses several security issues, such as access control and privacy leakage, as well as faces critical challenges to obtain efficient data analysis and services. In this article, we propose an efficient and privacy-preserving fog-assisted health data sharing (PFHDS) scheme for e-healthcare systems. Specifically, we integrate the fog node to classify the shared data into different categories according to disease risks for efficient health data analysis. Meanwhile, we design an enhanced attribute-based encryption method through combination of a personal access policy on patients and a professional access policy on the fog node for effective medical service provision. Furthermore, we achieve significant encryption consumption reduction for patients by offloading a portion of the computation and storage burden from patients to the fog node. Security discussions show that PFHDS realizes data confidentiality and fine-grained access control with collusion resistance. Performance evaluations demonstrate cost-efficient encryption computation, storage and energy consumption.
引用
收藏
页数:23
相关论文
共 45 条
[1]  
[Anonymous], 2013, ELECT J DIFFERENTIAL
[2]  
[Anonymous], 2015, P WORKSH IOT CHALL M
[3]  
Borgh J., 2017, 2017 Global Internet of Things Summit (GIoTS), P1, DOI [DOI 10.1109/GIOTS.2017.8016277, 10.1109/GIOTS.2017.8016277]
[4]   Machine Learning Classification over Encrypted Data [J].
Bost, Raphael ;
Popa, Raluca Ada ;
Tu, Stephen ;
Goldwasser, Shafi .
22ND ANNUAL NETWORK AND DISTRIBUTED SYSTEM SECURITY SYMPOSIUM (NDSS 2015), 2015,
[5]  
Briggs D, 2014, PALGRAVE PIVOT, P1, DOI 10.1057/9781137380616
[6]   Privacy Protection and Intrusion Avoidance for Cloudlet-Based Medical Data Sharing [J].
Chen, Min ;
Qian, Yongfeng ;
Chen, Jing ;
Hwang, Kai ;
Mao, Shiwen ;
Hu, Long .
IEEE TRANSACTIONS ON CLOUD COMPUTING, 2020, 8 (04) :1274-1283
[7]   Key-Aggregate Cryptosystem for Scalable Data Sharing in Cloud Storage [J].
Chu, Cheng-Kang ;
Chow, Sherman S. M. ;
Tzeng, Wen-Guey ;
Zhou, Jianying ;
Deng, Robert H. .
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2014, 25 (02) :468-477
[8]   Portal of medical data models: information infrastructure for medical research and healthcare [J].
Dugas, Martin ;
Neuhaus, Philipp ;
Meidt, Alexandra ;
Doods, Justin ;
Storck, Michael ;
Bruland, Philipp ;
Varghese, Julian .
DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION, 2016,
[9]   Cyber Security and the Role of Intelligent Systems in Addressing its Challenges [J].
Harel, Yaniv ;
Ben Gal, Irad ;
Elovici, Yuval .
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2017, 8 (04)
[10]   FSSR: Fine-Grained EHRs Sharing via Similarity-Based Recommendation in Cloud-Assisted eHealthcare System [J].
Huang, Cheng ;
Lu, Rongxing ;
Zhu, Hui ;
Shao, Jun ;
Lin, Xiaodong .
ASIA CCS'16: PROCEEDINGS OF THE 11TH ACM ASIA CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, 2016, :95-106