With the widespread of Internet of Things (IoT) environment, a big data concept has emerged to handle a large number of data generated by IoT devices. Moreover, since data-driven approaches now become important for business, IoT data markets have emerged, and IoT big data are exploited by major stakeholders such as data brokers and data service providers. Since many services and applications utilize data analytic methods with collected data from IoT devices, the conflict issues between privacy and data exploitation are raised, and the markets are mainly categorized as privacy protection markets and privacy valuation markets, respectively. Since these kinds of data value chains (which are mainly considered by business stakeholders) are revealed, data providers are interested in proper incentives in exchange for their privacy (i.e., privacy valuation) under their agreement. Therefore, this paper proposes a competitive data trading model that consists of data providers who weigh the value between privacy protection and valuation as well as other business stakeholders. Each data broker considers the willingness-to-sell of data providers, and a single data service provider considers the willingness-to-pay of service consumers. At the same time, multiple data brokers compete to sell their dataset to the data service provider as a non-cooperative game model. Based on the Nash Equilibrium analysis (NE) of the game, the feasibility is shown that the proposed model has the unique NE that maximizes the profits of business stakeholders while satisfying all market participants.

With the evolutionary development of the Internet of Things (IoT), the demand for delay-sensitive applications has increased. In this manner, mobile-edge computing (MEC) has emerged as a promising technology to run these applications on mobile devices. Several studies have been conducted on pricing schemes in the MEC environment, and they have focused on the amount of offloaded data in pricing. However, under the previously proposed pricing policy, the server resource usage of users is not properly reflected in the payment, so users only try to occupy the server resources as much as possible without any consideration. To solve this problem, this article newly proposes a differential pricing scheme in which the unit price per second is determined based on the user’s usage of server computational resources. Also, it suggests the user’s optimal offloading strategy and server’s equilibrium pricing strategy by formulating Stackelberg game based on the proposed pricing scheme and execution delay. Several numerical results show that the proposed pricing scheme and strategies can solve the problems and can increase the efficiency of edge server’s computational resources.

This paper proposes a profit maximization model for a data consumer when it buys personal data from data providers (by obtaining consent) through data brokers and provides their new services to data providers (i.e., service consumers). To observe the behavioral models of data providers, the data consumer, and service consumers, this paper proposes the willingness-to-sell model of personal data of data providers (which is affected by data providers’ behavior related to explicit consent), the service quality model obtained by the collected personal data from the data consumer’s perspective, and the willingness-to-pay model of service consumers regarding provided new services from the data consumer. Particularly, this paper jointly considers the behavior of data providers and service users under a limited budget. With parameters inspired by real-world surveys on data providers, this paper shows various numerical results to check the feasibility of the proposed models.

With the widespread of electric vehicles (EV), the necessity of installing EV charging facility has rapidly increased. Since operating EV charging facility are costly and complex, choosing a proper power operation scheme is critical in implementing a cost-effective charging service. Therefore, this article proposes an efficient power operation scheme for EV charging facility by jointly analyzing not only monetary issues with various power rate policies and battery wear-out costs of the energy storage system (ESS) but also the uncertainty issues caused by EV users' behavior under different charging requirements. By considering the issues, this article analyzes and formulates the problem with robust optimization techniques. By using the real-world EV operation datasets, the effectiveness of the proposed scheme is analyzed in terms of daily peak power and total cost. In addition, by applying the proposed scheme in an actual power system, it is shown that the proposed scheme is able to reduce the overall cost by 20.5% and reduce peak power by 17.3% compared to other benchmark models.

Detecting unusual behaviors of insiders on enterprise resource planning (ERP) systems is one of the essential parts to reduce the risks of threatening and abusing enterprise resources by insiders. Many approaches to detect the behaviors based on rule-based systems and stochastic processes are currently limited to empirical monitoring using manually established algorithms or probabilistic boundaries. Those approaches need prior knowledge such as user permission guideline and process data characteristics. Unfortunately, obtaining prior knowledge is hard in practice, and these are not appropriate to detect atypical unusual behavior which can not be clearly defined using heuristic rules. Therefore, in this article, we propose a novel framework for unusual insider behavior detection (UIBD) for ERP systems. The proposed framework initially derives a discriminative model for normal behavior samples, and UIBD is conducted by computing an error using the model. Since the model is compiled using normal samples only, the error of unusual samples would be larger than normal ones. To derive a robust normal behavior model, we present adversarial recurrent autoencoder (ARAE). To demonstrate the efficiency of the proposed framework based on ARAE, we conduct experiments using a dataset composed of insider behaviors defined by sequences of security audit logs of ERP systems operating in real-world enterprises. The experimental results show that the proposed framework with ARAE can successfully detect unusual insider behaviors and outperform other methods to detect unusual insider behavior or threatening.