Enabling Trusted and Privacy-Preserving Healthcare Services in Social Media Health Networks

Abstract

Social Media Health Networks provide a promising paradigm to attract patients to share and communicate their personal health status with other online patients, and consult healthcare services from online caregivers with social networks. Social Media Health Networks transform healthcare services from time-consuming offline hospital-centered paradigm to the convenient and efficient online paradigm through Internet, which can expand the traditional healthcare services and shorten the information gap between patients and caregivers. However, how to build the trust between patients and caregivers raises a challenging issue due to the openness of the social networks; meanwhile, the personal privacy may be disclosed when sharing personal health information with other patients and caregivers. In this paper, we propose a personalized and trusted healthcare service approach to enable trusted and privacy-preserving healthcare services in social media health networks, which can improve the trustiness between patients and caregivers through authentic ratings toward caregivers and guarantee the patients' privacy. Specifically, we employ the collaborative filtering model to seek appropriate personalized caregivers, bloom filter to extract and map the personal healthcare symptoms, and inner product to compute the similarity between patients for finding patients with similar health symptoms in a privacy-preserving way. Meanwhile, to guarantee authentic ratings and reviews toward caregivers, we develop a sybil attack detection scheme to find patients' fake ratings and reviews using different pseudonyms. Security analysis shows that our proposed approach can preserve the privacy of patients and prevent sybil attacks. Performance evaluation demonstrates that our approach can achieve prominent performance improvement, in terms of personalized caregivers finding and sybil attack resistance.