At a crossroads? Chief Editors Elena Ferrari and Murat Kantarcioglu on the Future of Cybersecurity and Privacy in Big Data
by Louisa Wood
The transformative power of artificial intelligence and Big Data holds vast promise and the expanding power of the data-driven economy is expected to produce big economic gains. However, this new frontier has also stirred up major security challenges. Recent scandals, as well as the EU’s upcoming new rules around data protection, have prompted a moment of reckoning. Consumers are now increasingly aware of the importance of personal security with policy-makers forced to take careful approaches to ensure they shield private data from misuse.
Frontiers asked the Chief Editors for Cybersecurity and Privacy in Frontiers in Big Data – Elena Ferrari and Murat Kantarcioglu, for their take on privacy issues surrounding Big Data management and how research is developing in this field.
Big data, big risk. How can we be smart about our personal security in the face of the big data revolution?
Big Data, big risk, but also big opportunities. Despite all the privacy and security risks, Big Data offers incredible opportunities to all of us. What we should learn is to benefit from Big Data services by also being conscious of the value of our personal data and how to protect it.
Today we are at a crossroads: either our digital society will definitively evolve into a “Big Brother” society, where a few providers possess the majority of available personal data and exploit them as they like; or we can evolve towards a more ethical and privacy-conscious use of data, where privacy and utility can co-exist and where the benefits of Big Data are brought back to their owners. Making this possible is not only a technical issue, but also has political, economic, and legal issues that need to be solved. However, as computer scientists, we can give contribute enormously by working towards making this second option technically possible.
What’s the hottest topic in your research area regarding privacy and security issues?
One of the areas receiving the most attention at present is the use of decentralized privacy preserving architectures for Big Data management. In theory, decentralization mitigates one of the biggest threats to consumer privacy by ensuring that one single entity does not own what are typically unprecedented collections of personal data in their amount, variety, geographical span, and richness in detail. Nevertheless, decentralization also comes with its own Pandora’s Box of challenges, which a lot of research is currently focused on addressing.
Why do we need a section on Cybersecurity and Privacy in Frontiers in Big Data and what is your united vision for this section?
Although a host of techniques have been developed that try to protect “Big Data” against misuse and provide guarantees regarding individual privacy, more research is needed to address the emerging challenges. There is a particular 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 over how their data is used and, if needed, track and ask for 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.
The motivation behind this section is to provide an inter-disciplinary platform to address these challenges from an array of different perspectives – from the computer science field to the psychological.
The Cybersecurity and Privacy section of Frontiers in Big Data welcomes high-quality article submissions and Research Topic proposals on a wide range of topics. The Research Topic Secure Privacy-preserving Machine Learning is open for submissions.
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