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PPM-HDA: privacy-preserving and multifunctional health data aggregation with fault tolerance

Han, Song, Zhao, Shuai, Li, Qinghua, Ju, Chun-Hua and Zhou, Wanlei 2016, PPM-HDA: privacy-preserving and multifunctional health data aggregation with fault tolerance, IEEE transactions on information forensics and security, vol. 11, no. 9, pp. 1940-1955, doi: 10.1109/TIFS.2015.2472369.

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Title PPM-HDA: privacy-preserving and multifunctional health data aggregation with fault tolerance
Author(s) Han, Song
Zhao, Shuai
Li, Qinghua
Ju, Chun-Hua
Zhou, Wanlei
Journal name IEEE transactions on information forensics and security
Volume number 11
Issue number 9
Start page 1940
End page 1955
Total pages 16
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2016-09
ISSN 1556-6013
1556-6021
Keyword(s) multifunctional aggregation
differential privacy
spatial aggregation
temporal aggregation
fault tolerance
privacy-preserving
cloud assisted WBANs
Summary Wireless body area networks (WBANs), as a promising health-care system, can provide tremendous benefits for timely and continuous patient care and remote health monitoring. Owing to the restriction of communication, computation and power in WBANs, cloud-assisted WBANs, which offer more reliable, intelligent, and timely health-care services for mobile users and patients, are receiving increasing attention. However, how to aggregate the health data multifunctionally and efficiently is still an open issue to the cloud server (CS). In this paper, we propose a privacy-preserving and multifunctional health data aggregation (PPM-HDA) mechanism with fault tolerance for cloud-assisted WBANs. With PPM-HDA, the CS can compute multiple statistical functions of users' health data in a privacy-preserving way to offer various services. In particular, we first propose a multifunctional health data additive aggregation scheme (MHDA+) to support additive aggregate functions, such as average and variance. Then, we put forward MHDA as an extension of MHDA+ to support nonadditive aggregations, such as min/max, median, percentile, and histogram. The PPM-HDA can resist differential attacks, which most existing data aggregation schemes suffer from. The security analysis shows that the PPM-HDA can protect users' privacy against many threats. Performance evaluations illustrate that the computational overhead of MHDA+ is significantly reduced with the assistance of CSs. Our MHDA scheme is more efficient than previously reported min/max aggregation schemes in terms of communication overhead when the applications require large plaintext space and highly accurate data.
Language eng
DOI 10.1109/TIFS.2015.2472369
Field of Research 080501 Distributed and Grid Systems
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30089403

Document type: Journal Article
Collection: School of Information Technology
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