Applying artificial intelligence (AI) to data from Industrial Internet of Things (IIoT) devices is a novel direction in geological studies. However, privacy and security concerns hinder the sharing of data, thus affecting the performance of current AI-based approaches. In this article, we propose a novel data management style to address the privacy and security issues in joint hydrocarbon explorations. Federated learning can facilitate the analysis of multiple datasets without the need to share them, protecting private information of different companies in a virtual joint venture. We use the inference of petroleum reservoirs in karst stratigraphy as a case study. A federated learning-based enterprise data management framework is proposed to virtually integrate the information from different organizations. Our key contributions are summarized as follows. 1) A method for karst identification and inference is proposed, which uses neural networks to recognize the size of petroleum reservoirs in different karst areas. 2) A federated learning algorithm is applied to virtually aggregate data samples from different companies. 3) The performance of the new privacy-preserving integration model is compared with those of the individual/local deep learning models. Our results show that the proposed approach can substantially improve the accuracy of petroleum reservoir explorations.

With the ubiquity of social media, privacy leakage has become a urgent problemfor social media managers. Studying how the privacy information diffuses through social media has attracted much attention. As a prerequisite, modeling privacy information diffusion is important research. Current approaches for modeling information diffusion are not available for privacy information since they did not consider the propagation features of privacy information in social media. Thispaper discusses the problem of modeling privacy information in social media and its challenges. We first analyse the information diffusion paths in the basic parameters of complex network and the high-order structures. We find that the privacy information is different in propagation features and the size of star structures. Second, a new information diffusion model is illustrated to simulate the diffusion process of information in social media by considering the following three parameters: 1) the probability of users receiving this message, 2) the probability that users have a tendency to forward this message and 3) the interest the users hold for this message. Finally, a block mechanism is designed to congest the diffusion of privacy information in social media.

Social media has become a ubiquitous tool for spreading news, messages, and generally allowing for communication between individuals. Hence, studying how our privacy information might also spread across social media is important research. To date, many studies have used information diffusion models to simulate and then examine how information flows through social networks. But these models are theoretical, and newsworthy information may not behave in the same way as privacy information, raising the question: Are the observed phenomena indicative of real privacy propagation? To explore this question, we assembled a dataset from Twitter comprising propagated information flows for both private and normal information. We then built a graph convolutional network to trace and classify differences in the way each type of information spreads throughout the platform. The results reveal that there are indeed key differences in the diffusion processes of the two types of information. More importantly, we design privacy-preserving methods to reduce the privacy propagation in social media.

Summary The growing popularity of users in online social network gives a big opportunity for online auction. The famous Information Diffusion Mechanism (IDM) is an excellent methods even meet the incentive compatibility and individual rationality. Although the existing auction in online social network has considered the buyers' information has not known by the seller, current mechanism still cannot preserve the information such as prices. In this paper, we propose a novel mechanism which modeled the auction process in online social network and preserved users' privacy by using differential privacy mechanism. Our mechanism can successfully process the auction and at the same time preserve clients' price information from neighbors. We achieved these by adding Laplace noise for its valuation and the number of valuation seller received in the auction process. We also formulate this mechanism on the real network to show the feasibility and effective of the proposed mechanism.

Networks that constantly transmit information and change structure are becoming increasingly prevalent. However, traditional privacy models are designed to protect static information, such as records in a database or a person’s profile information, which seldom changes. This conflict between static models and dynamic environments is dramatically hindering the effectiveness and efficiency of privacy preservation in today’s dynamic world. Hence, in this paper, we formally define the concept of dynamic privacy, present two novel perspectives, privacy propagation and accumulation, on the way private information can spread through dynamic cyberspace, and develop associated theories and mechanisms for preserving privacy in advanced complex networks, such as social networking sites where data are constantly being released, shared, and exchanged.

Summary Studying the influence of negative words that spread during election period is an important work in social media. Most of current methods rely on sentiment analysis of tweets to determine the users' preference. However, sentiment analysis can only makes use of emotional words (ie, adverbs and adjectives), which only take 30 percent of the context in the Internet. According to our empirical analysis based on real datasets, the bias on word selection largely reduced the accuracy of the context in the Internet. In order to address this critical problem, we propose a new method that makes use of nouns with emotional context to determine the election preference of each user. By collecting the frequencies of words in context, we weigh the impact of each supportive/objective noun to strengthen the determination of users' preference. Final results will further be integrated to examine the effectiveness and efficiency of our proposed method. To indicate this idea, we collect and adopt real datasets (UK Prime Minister 2017 and US President Campaign 2016) in the experiments. All the experiment results suggested that our integrated method largely outperformed previous prediction methods. In particular, the prediction results were quite similar to the final results of the UK and US election. Meanwhile, for UK election, we found that the daily approval rate is closely related to the event happened everyday.