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

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. Index Terms-Social network, infection analysis, privacy preservation! 1545-5971 (c)

Privacy issues in social networks and analysis: a comprehensive survey

“…One more example of social network applications is using social network data to analyse human-to-human infection. Zhang et al [66] suggested a human-to-human infection analysis method through combining health data and social network data without leaking patients' private information. Thus, it can be seen that the privacy issue is always a significant problem in applications which are based on social networks because the social network data is sensitive and its privacy can be breached easily.…”

“…Many other solutions have been proposed to protect the privacy for users in social network applications such as a filtering scheme to filter spams in social networks without revealing creators’ private information such as keywords to filter holders [1] or using social network data to analyse human‐to‐human infection without leaking private information of patients [66].…”

A Cloud-Based Secure and Privacy-Preserving Clustering Analysis of Infectious Disease

“…To preserve the data privacy, pseudonyms or data encryption/obfuscation are commonly adopted approaches. Depending on different application scenarios, for instance, some literature may consider cloud servers are limited to access the ciphertext [20], [21]. In this work, we leverage the pseudonym crypto-system and the cloud servers have users' plaintext data so that on one hand they could provision users services while on the other hand allow PHO to conduct infectious disease analysis.…”

A Classified Protection Protocol for RFID-based Medical Systems

“…It makes remote real-time monitoring and telemedicine become a reality for Wireless Body Area Networks (WBAN) (He et al, 2013) and mobile health networks (Zhang et al, 2015). Patients authorize doctors to monitor users' physical health data through RFID system (Zhang et al, 2016). For medical assets and medication, RFID systems can track and manage.…”