As recent trends show, capturing, storing, and mining "big data" may create significant value in industries ranging from healthcare, business, and government services to the entire science spectrum. At the same time, privacy and security concerns may limit data sharing and data use. Even worse, as recent events showed, private data may be hacked, and misused. Although, there has been a plethora of techniques developed that try to protect "big data" against misuse and provide guarantees about individual privacy, more research is needed to address the emerging challenges. Especially, there is a need for new approaches to protect sensitive data that is flexible and adaptive enough to encourage innovation, but also protects the rights of individuals and organizations. Such approaches will need to give users more control of their personal data use. Once data is collected and shared, users should retain control on how their data is used and, if needed, track and ask the deletion of their personal data. Such a vision requires innovative solutions in many areas including personal data storage, data provenance, privacy risk estimation, privacy-aware data sharing policies, cryptographic tools for personal data sharing, protecting big data against hacking, novel access control techniques for big data, among others. Furthermore, different aspects ranging from economical models for data privacy to usability of such proposed techniques need to be explored.
The section is especially interested in articles addressing emerging challenges in big data security and privacy. In addition, innovative interdisciplinary approaches that address these challenges using combinations of economic, psychological, and computer science techniques are highly encouraged.
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Cybersecurity and Privacy welcomes submissions of the following article types: Correction, Data Report, Editorial, General Commentary, Hypothesis and Theory, Methods, Mini Review, Opinion, Original Research, Perspective, Review, Specialty Grand Challenge and Technology and Code.
All manuscripts must be submitted directly to the section Cybersecurity and Privacy, where they are peer-reviewed by the Associate and Review Editors of the specialty section.
Articles published in the section Cybersecurity and Privacy will benefit from the Frontiers impact and tiering system after online publication. Authors of published original research with the highest impact, as judged democratically by the readers, will be invited by the Chief Editor to write a Frontiers Focused Review - a tier-climbing article. This is referred to as "democratic tiering". The author selection is based on article impact analytics of original research published in all Frontiers specialty journals and sections. Focused Reviews are centered on the original discovery, place it into a broader context, and aim to address the wider community across all of Big Data.
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