Xueqin Liang Ms.
2018-11-23 13:09:02 UTC
Call For Paper: a Special Issue of Information Sciences On Big Data Privacy
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 interation, 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 networking, 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 inefficient, especially for big data. Preserving privacy of big data introduces additional challenges with regard to computational complexity, efficiency, adaptability, personality, flexibility, fine-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: October 1st, 2018 extended to December 1st, 2018
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: ***@gmail.com
Prof. Robert H. Deng, Singapore Management University, Singapore, Email: ***@smu.edu.sg
Prof. Elisa Bertino, Purdue University, USA, Email: ***@purdue.edu
_______________________________________________
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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 interation, 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 networking, 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 inefficient, especially for big data. Preserving privacy of big data introduces additional challenges with regard to computational complexity, efficiency, adaptability, personality, flexibility, fine-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: October 1st, 2018 extended to December 1st, 2018
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: ***@gmail.com
Prof. Robert H. Deng, Singapore Management University, Singapore, Email: ***@smu.edu.sg
Prof. Elisa Bertino, Purdue University, USA, Email: ***@purdue.edu
_______________________________________________
Please do not post msgs that are not relevant to the database community at large. Go to www.cs.wisc.edu/dbworld for guidelines and posting forms.
To unsubscribe, go to https://lists.cs.wisc.edu/mailman/listinfo/dbworld