Paper Info
Title
A Cloud-Based Secure and Privacy-Preserving Clustering Analysis of Infectious Disease
Abstract
The early detection of where and when fatal infectious diseases outbreak is of critical importance to the public health. To effectively detect, analyze and then intervene the spread of diseases, people's health status along with their location information should be timely collected. However, the conventional practices are via surveys or field health workers, which are highly costly and pose serious privacy threats to participants. In this paper, we for the first time propose to exploit the ubiquitous cloud services to collect users' multi-dimensional data in a secure and privacy-preserving manner and to enable the analysis of infectious disease. Specifically, we target at the spatial clustering analysis using Kulldorf scan statistic and propose a key-oblivious inner product encryption (KOIPE) mechanism to ensure that the untrusted entity only obtains the statistic instead of individual's data. Furthermore, we design an anonymous and sybil-resilient approach to protect the data collection process from double registration attacks and meanwhile preserve participant's privacy against untrusted cloud servers. A rigorous and comprehensive security analysis is given to validate our design, and we also conduct extensive simulations based on real-life datasets to demonstrate the performance of our scheme in terms of communication and computing overhead.
Year
DOI
Venue
2018
10.1109/PAC.2018.00017
2018 IEEE Symposium on Privacy-Aware Computing (PAC)
Keywords
DocType
ISBN
public health, clustering analysis, Kulldorf scan statistic, group signature, identity-based encryption, secure multi party computation
Conference
978-1-5386-8443-6
Citations 
PageRank 
References 
0
0.34
5
Authors
5
Name
Order
Citations
PageRank
Jianqing Liu100.34
Yaodan Hu233.08
Hao Yue344656.19
Yanmin Gong413316.82
Yuguang Fang56982476.76