ABSTRACT:
Human-to-human infection, as a type
of fatal public health threats, can rapidly spread in a human population,
resulting in a large amount of labor and health cost
for treatment, control and prevention. To slow down the spread of infection,
social network is envisioned to provide detailed contact statistics to isolate susceptive people who has frequent contacts with infected
patients. In this paper, we propose a novel human-to-human infection analysis
approach by exploiting social network data and health data that are collected
by social network and e-healthcare technologies. We enable the social cloud
server and health cloud server to exchange social contact information of
infected patients and user’s health condition in a privacy-preserving way.
Specifically, we propose a privacy-preserving data query method based on
conditional oblivious transfer to guarantee that only the authorized entities
can query users’ social data and the social cloud server cannot infer anything
during the query. In addition, we propose a privacy-preserving
classification-based infection analysis method that can be performed by untrusted cloud servers without accessing the users’ health
data. The performance evaluation shows that the proposed approach achieves
higher infection analysis accuracy with the acceptable computational overhead.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
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System : Pentium Dual Core.
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Hard Disk : 120 GB.
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Monitor : 15’’ LED
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Input Devices : Keyboard, Mouse
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Ram : 1 GB
SOFTWARE REQUIREMENTS:
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Operating system : Windows 7.
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Coding Language : JAVA/J2EE
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Tool : Netbeans 7.2.1
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Database : MYSQL
REFERENCE:
Kuan Zhang, Student Member, IEEE, Xiaohui Liang, Member, IEEE, Jianbing Ni, Kan Yang, and Xuemin
(Sherman) Shen Fellow, IEEE, “Exploiting
Social Network to Enhance Human-to-Human Infection Analysis Without Privacy
Leakage”, IEEE Transactions on Dependable and Secure Computing, 2018