(Co-located with NETYS 2017, Morocco)
Scope and Objectives:
Recent innovations such as cloud computing, mobile computing, distributed computing, online social networks and Internet of Things have played a major role in the development of Big Data technologies. The dramatically increasing demands of big data have driven a remarkable growth of the implementations of platforms for data processing and mining. These platforms must have the ability to analyze the stream data as fast as they are captured. Therefore, specific algorithms and techniques (e.g. machine learning) are required to use big data effectively.
On the other hand, big data brings technical challenges for data processing with respect to security and privacy. Big data applications make use of distributed computing frameworks to process the massive amount of data. This creates new opportunities for breaches of security. Maintaining security in distributed computing frameworks is a key to secure solutions involving big data.
Moreover, the growing interest in publishing statistics, analyses, and raw data raises privacy concerns as well. Removing personally identifiable information has not been able to address the privacy risks of large-scale data collection, analytics, and release. In this direction, the development of solutions that better protect privacy and allow robust and precise negotiation of privacy expectations is of great interest.
The International Workshop on Big Data Analytics, Security and Privacy (BiDAS 2017) addresses relationships between big data analytics, security and privacy. The workshop aims to bring together leading scientists and experts from academia and industry to discuss the current status of big data analytics and present potential ways to address security and privacy in big data environments.
Topics of interest for the workshop include, but are not limited to:
- Big Data Analytics:
- Algorithms and Techniques for Big Data Processing
- Streaming data and Real-time analysis
- Machine Learning & Deep Learning
- Social Networks Analysis
- Big Data Security and Privacy:
- Privacy Preserving Big Data Analytics
- Usable Security and Privacy for Big Data
- Big Data System Security and Integrity
- Big Data Information Security