The massive deployment of networking, communications and computing technologies has brought us into the era of big data. Huge volumes of data are today generated and collected due to human-computer interaction, device-device communications, data outsourcing, environment sensing and behavior monitoring. Many such data often encode privacy-sensitive information related to individuals and support the inference of a large variety of privacy-sensitive information through the use of data analytics, data mining and machine learning. Thus, preserving privacy in the context of big data is a critical requirement in cyber-space. Obviously, preserving privacy of big data is even more challenging when dealing with many emerging technologies, e.g., Internet of Things (IoT), cloud computing, edge computing, crowdsourcing and crowdsensing, social net-working, and next generation communication systems. Although technologies and theories are widely studied and applied to ensure data privacy in recent years, existing solutions are still inef-ficient, especially for big data. Preserving privacy of big data introduces additional challenges with regard to computational complexity, efficiency, adaptability, personality, flexibility, fi-ne-graininess and scalability. Big data privacy promises many novel solutions and at the same time, many challenges should also be overcome.

This special issue aims to bring together researchers and practitioners to discuss various aspects of big data privacy, explore key theories, investigate significant algorithms, protocols and schemes and innovate new solutions for overcoming major challenges in this significant research area.

Topics include but are not limited to:

• Theoretical aspects of big data privacy

• Privacy-preserving computing models and techniques

• Fine-grained and personalized privacy preservation 

• Privacy auditing and provenance management on big data

• Adaptive privacy preservation on big data

• Scalability of big data privacy protection

• Big data privacy protection based on blockchain

• Secure big data computation and verification

• Privacy-preserving big data search and query

• Privacy preservation in big data fusion

• Privacy-preserving machine learning and data mining

• Privacy digitalization and computation 

• Economic studies on big data privacy

Important Dates

Paper submission due: December 1st, 2018 (extended)

Notification of decision: February 1st, 2019

Revision due: May 1st, 2019

Acceptance notification: July 1st, 2019

Approximate publication date: Late 2019, subject to journal publication schedules

Submission Format

Author guidelines for preparation of manuscript can be found at www.elsevier.com/locate/ins

Submission Guidelines

All manuscripts and any supplementary material should be submitted through Elsevier Editorial System (EES). The authors must select “VSI:BigDataPrivacy” when they identify the “Article Type” step in the submission process. The EES website is located at http://ees.elsevier.com/ins/

Guest Editors 

Prof. Zheng Yan, Xidian University, China & Aalto University, Finland, Email: zheng-yan.pz@gmail.com

Prof. Robert H. Deng, Singapore Management University, Singapore, Email: rob-ertdeng@smu.edu.sg

Prof. Elisa Bertino, Purdue University, USA, Email: bertino@purdue.edu