No abstract

“Social sensors” refer to those who provide opinions through electronic communication channels such as social networks. There are two major issues in current models of sentiment analysis in social sensor networks. First, most existing models only analyzed the sentiment within the text but did not analyze the users, which led to the experimental results difficult to explain. Second, few studies extract the specific opinions of users. Only analyzing the emotional tendencies or aspect-level emotions of social users brings difficulties to the analysis of the opinion evolution in public emergencies. To resolve these issues, we propose an explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors. Our model extracts the specific opinions of the user groups on the topics and fully considers the impacts of their diverse features on sentiment analysis. We conduct experiments on 51,853 tweets about the “COVID-19” collected from 1 May 2020 to 9 July 2020. We build users’ portraits from three aspects: attribute features, interest features, and emotional features. Six machine learning algorithms are used to predict emotional tendency based on users’ portraits. We analyze the influence of users’ features on the sentiment. The prediction accuracy of our model is 64.88%.

Blockchain technology has been widely used in digital currency, Internet of Things, and other important fields because of its decentralization, nontampering, and anonymity. The vigorous development of blockchain cannot be separated from the security guarantee. However, there are various security threats within the blockchain that have shown in the past to cause huge financial losses. This paper aims at studying the multi-level security threats existing in the Ethereum blockchain, and exploring the security protection schemes under multiple attack scenarios. There are ten attack scenarios studied in this paper, which are replay attack, short url attack, false top-up attack, transaction order dependence attack, integer overflow attack, re-entrancy attack, honeypot attack, airdrop hunting attack, writing of arbitrary storage address attack, and gas exhaustion denial of service attack. This paper also proposes protection schemes. Finally, these schemes are evaluated by experiments. Experimental results show that our approach is efficient and does not bring too much extra cost and that the time cost has doubled at most.

Usage‐based insurance (UBI) provides reasonable vehicle insurance premiums based on vehicle usage and driving behavior. In general, there are three major issues in realizing intelligent UBI systems. First, UBI evaluation mechanisms are not auditable to drivers. Insurers may thus deliberately adjust the UBI premiums. Second, the process of collecting driving data by insurers may lead to serious privacy breaches. Third, forging safer driving data for reducing insurance premiums may cause economic losses for insurers. To address these challenges, in this study, we propose CCUBI, a cross‐chain‐based premium competition scheme with privacy preservation for intelligent UBI systems. We introduce tamper‐resistant blockchain and smart contracts to construct credible insurance mechanisms. The cross‐chain technology connects these blockchains in the entire network to form an open premium competition scheme. Vehicle owners can assess designated insurers by sharing historical data with them to get a suitable CCUBI plan. In addition, we propose a data aggregation method used for CCUBI analysis with privacy preservation. Vehicle owners only publish proofs of the driving data. Proofs can still maintain privacy and computability in cross‐chain flows. Finally, we adopt roadside units to detect forged driving data. We conduct a detailed security analysis. Experimental results also demonstrate the efficiency of CCUBI.