The integration of extensive parallel computation power, scalable platforms, and advanced communications has profoundly unleashed the potentials of big data mining in recent years. A large number of cloud datacenters have been established around the globe and provide necessary tools and infrastructure to utilize economical, on-demand, rapid-elasticity computation and storage services. With these, an increasingly huge amount of data of various types and formats have been collected from many different sources such as online social media, Internet of Things (IoT), mobile devices, and genome projects via both wired and wireless communication channels. Unlocking the value of big data through analytics and mining has been regarded as the key enabler to many innovation and marketing strategies which, in turn, has pushed more efforts and supports to the big data related R&D. As an evidence, for example, Gartner has recently reported that most of the world’s largest 200 companies have plans to invest in the development of intelligent apps as well as to utilize the full toolkit of big data and analytics tools by 2018. New founding from these investments then is to be incorporated to refine the services offered by companies and improve customer experience. This illustrates that an extensive research is expected to be more actively supported for big data mining infrastructure, platforms, and applications that runs both on wired and on wireless communication channels in order to conduct more efficient knowledge discovery and smart decision support.

One of the major concerns in big data mining approach is with security and privacy. With big data applications such as online social media, mobile services, and smart IoT widely adopted in our daily life, an enormous amount of data has been generated based on various aspects of the individuals. Without a proper security and privacy protection in all aspects of computing environment including communication environment, this can be disclosed intentionally or unintentionally, posing severe threats on the individuals. Moreover, as the storage, delivery, management, processing, and mining of such massive data sets are often outsourced to cloud datacenters, traditional security solutions confined within a well-defined security perimeter fail to be applied in such open and sharing environments. These security and privacy issues pose tremendous barriers to taking advantages from the full use of our huge data assets. As such, it is high time to investigate the security and privacy issues in big data mining by examining big data infrastructure, platforms, and applications in detail hence for the call for this special issue.

Potential topics include but are not limited to the following:

• Fundamental privacy related theory related to big data mining
• Differentially private big data mining
• Privacy-preserving big data delivery via wired and wireless communication networks
• Privacy-preserving big data sharing, publishing, and mining
• Private big data mining with IoT infrastructure and cloud and fog computing
• Privacy issues in cloud networking services
• Privacy issues in big data processing paradigms or platforms (e.g., MapReduce)
• Privacy issues in machine (deep) learning algorithms, models, and applications
• Privacy-as-a-Service