In this paper, a privacy-preserving smart IoT-based healthcare big data storage system with self-adaptive access control is proposed. The aim is to ensure the security of patients' healthcare data, realize access control for normal and emergency scenarios, and support smart deduplication to save the storage space in big data storage system. The medical files generated by the healthcare IoT network are encrypted and transferred to the storage system, which can be securely shared among the healthcare staff from different medical domains leveraging a cross-domain access control policy. The traditional access control technology allows the authorized data users to decrypt patient's sensitive medical data, but also hampers the first-aid treatment when the patient's life is threatened because the on-site first-aid personnel are not permitted to get patient's historical medical data. To deal with this dilemma, we propose a secure system to devise a novel two-fold access control mechanism, which is self-adaptive for both normal and emergency situations. In normal application, the healthcare staff with proper attribute secret keys can have the data access privilege; in emergency application, patien-

Social network has become a very popular way for internet users to communicate and interact online. Users spend plenty of time on famous social networks (e.g., Facebook, Twitter, Sina Weibo, etc.), reading news, discussing events and posting messages. Unfortunately, this popularity also attracts a significant amount of spammers who continuously expose malicious behavior (e.g., post messages containing commercial URLs, following a larger amount of users, etc.), leading to great misunderstanding and inconvenience on users' social activities. In this paper, a supervised machine learning based solution is proposed for an effective spammer detection. The main procedure of the work is: first, collect a dataset from Sina Weibo including 30,116 users and more than 16 million messages. Then, construct a labeled dataset of users and manually classify users into spammers and non-spammers. Afterwards, extract a set of feature from message content and users' social behavior, and apply into SVM (Support Vector Machines) based spammer detection algorithm. The experiment shows that the proposed solution is capable to provide excellent performance with true positive rate of spammers and non-spammers reaching 99.1% and 99.9% respectively.

In this paper, we propose a privacy-preserving e-health system, which is a fusion of Internet-of-things (IoT), big data and cloud storage. The medical IoT network monitors patient's physiological data, which are aggregated to electronic health record (EHR). The medical big data that contains a large amount of EHRs are outsourced to cloud platform. In the proposed system, the patient distributes an IoT group key to the medical nodes in an authenticated way without interaction round. The IoT messages are encrypted using the IoT group key and transmitted to the patient, which can be batch authenticated by the patient. The encrypted EHRs are shared among patient and different data users in a fine-grained access control manner. A novel keyword match based policy update mechanism is designed to enable flexible access policy updating without privacy leakage. Extensive comparison and simulation results demonstrate that the algorithms in the proposed system are efficient. Comprehensive analysis is provided to prove its security.

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Vehicular ad hoc networks (VANETs) have become important components of metropolitan area networks, and clustering for VANETS provides many advantages. However, the stability of current clustering algorithms exhibits poor robustness because a VANET is a highly dynamic scenario. In this study, a novel multi-hop clustering scheme for VANETs, which generates cluster heads (CHs) via neighborhood follow relationship between vehicles, is proposed. The scheme is based on a reasonable assumption that a vehicle cannot certainly identify which vehicle in its multi-hop neighbors is the most suitable to be its CH, but it can easily grasp which vehicle in one-hop distance is the most stable and similar with it, and thus, they most likely belong to the same cluster. Consequently, a vehicle can choose its CH by following the most stable vehicle. The relative mobility between two vehicles combining the gains based on the followed number and the historical following information enables a vehicle to select which target to follow. Extensive simulation experiments are conducted to validate the performance of the proposed clustering scheme.

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