Exploiting Social Network to Enhance Human-to-Human Infection Analysis without Privacy Leakage

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Exploiting Social Network to Enhance Human-to-Human Infection Analysis Without Privacy Leakage

Exploiting Social Network to Enhance Human-to-Human Infection Analysis without Privacy Leakage

Abstract 

Exploiting Social Network to Enhance Human-to-Human Infection Analysis

Exploiting Social Network to Enhance Human-to-Human Infection Analysis Without Privacy Leakage.In this paper, we propose a novel human-to-human infection analysis approach by exploiting social network data
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.
The performance evaluation shows that the proposed approach achieves higher infection analysis accuracy with the acceptable computational overhead.
we analyze the spread process of infectious disease with the consideration of user’s social contact and health condition.
We also utilize naive Bayesian classification method to enhance infection analysis with the collaboration of social and health cloud servers.

Conclusion

In this paper, we have proposed a First, we have analyzed the infectious disease spread process and adopted naive Bayesian classification to detect user’s infection status.

 Furthermore, we have exploited social cloud server to collect users’ social networking data, and relied on health cloud server to process/classify users’ health data.

We have also proposed a privacy-preserving classification-based infection analysis method to perform infection analysis over the encrypted social

and health data on the health cloud server.

Performance evaluation has demonstrated that the PIA can enhance infection analysis efficiency with acceptable overhead.

For the future work, we will develop deep learning algorithms for the PIA to perform the comprehensive analysis.