Data has revolutionised the digital ecosystem. Readily available large datasets foster AI and machine learning automated solutions.
The data generated from diverse and varied sources including IoT, social platforms, healthcare, system logs, bio-informatics, etc. contribute to and define the ethos of Big Data which is volume, velocity and variety.
Data lakes formed by the amalgamation of data from these sources require powerful, scalable and resilient storage and processing platforms to reveal the true value hidden inside this data mine.
Data formats and their collection from various sources not only introduce unprecedented challenges to different domains including IoT, manufacturing, smart cars, power grids etc., but also highlight the security and privacy issues in this age of big data.
Security and privacy in big data are facing many challenges, such as generative adversary networks, efficient encryption and decryption algorithms, encrypted information retrieval, attribute-based encryption, attacks on availability, and reliability.
Providing security and privacy for big data storage, transmission, and processing have been attracting much attention in all big data related areas.
Together big data and AI have created profound opportunities in every field, enabling the discovery of previously hidden patterns (including zero-day) and the development of new insights to inform decisions.
At the same time, protecting the information from cyber threats remains an urgent priority so using big data tools and AI techniques to enhance cybersecurity is a natural development.