The development of the Internet of Things has made extraordinary progress in recent years in both academic and industrial fields. There are quite a few smart home systems (SHSs) that have been developed by major companies to achieve home automation. However, the nature of smart homes inevitably raises security and privacy concerns. In this paper, we propose an improved energy-efficient, secure, and privacy-preserving communication protocol for the SHSs. In our proposed scheme, data transmissions within the SHS are secured by a symmetric encryption scheme with secret keys being generated by chaotic systems. Meanwhile, we incorporate message authentication codes to our scheme to guarantee data integrity and authenticity. We also provide detailed security analysis and performance evaluation in comparison with our previous work in terms of computational complexity, memory cost, and communication overhead.

With the popularity of mobile devices, mobile social networks (MSNs) have become an important platform for information dissemination. However, the spread of rumors in MSNs present a massive social threat. Currently, there are two kinds of methods to address this: blocking rumors at influential users and spreading truth to clarify rumors. However, most existing works either overlook the cost of various methods or only consider different methods individually. This paper proposes a heterogeneous-network-based epidemic model that incorporates the two kinds of methods to describe rumor spreading in MSNs. Moreover, two cost-efficient strategies are designed to restrain rumors. The first strategy is the real-time optimization strategy that minimizes the rumor-restraining cost by optimally combining various rumor-restraining methods such that a rumor can be extinct within an expected time period. The second strategy is the pulse spreading truth and continuous blocking rumor strategy that restrains rumor spreading through spreading truth periodically. The two strategies can restrain rumors in a continuous or periodical manner and guarantee cost efficiency. The experiments toward the Digg2009 data set demonstrate the effectiveness of the proposed model and the efficiency of the two strategies.

Due to the prominent development of public transportation systems, the taxi flows could nowadays work as a reasonable reference to the trend of urban population. Being aware of this knowledge will significantly benefit regular individuals, city planners, and the taxi companies themselves. However, to mindlessly publish such contents will severely threaten the private information of taxi companies. Both their own market ratios and the sensitive information of passengers and drivers will be revealed. Consequently, we propose in this work a novel framework for privacy-preserved traffic sharing among taxi companies, which jointly considers the privacy, profits, and fairness for participants. The framework allows companies to share scales of their taxi flows, and common knowledge will be derived from these statistics. Two algorithms are proposed for the derivation of sharing schemes in different scenarios, depending on whether the common knowledge can be accessed by third parties like individuals and governments. The differential privacy is utilized in both cases to preserve the sensitive information for taxi companies. Finally, both algorithms are validated on realworld data traces under multiple market distributions.