Qin Liu
2018-12-02 00:56:25 UTC
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[Apologies for multiple postings]
https://www.journals.elsevier.com/journal-of-parallel-and-distributed-computing/call-for-papers/security-privacy-in-social-big-data
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Journal of Parallel and Distributed Computing
Security & Privacy in Social Big Data
------------------------------------------------------------------------
SCOPE of the SI
The rapid development of social networks dramatically changes the way people think, work, and interact. As more and more individual users proactively generate, share, and exchange digital contents through social media, social networks have become a key source of big data. However, with such vast interconnectivity, convergence of relationships, and shared user information comes increased security and privacy concerns in social big data. On one hand, users carelessly posting their personal information on social media which can easily have their privacy breached. On the other hand, malicious attackers may manipulate such information to make a profit.
There are two important security and privacy issues in social networks. The first is how to effectively utilize social data while protecting user privacy. The second is how to guarantee the authenticity of social data for an in-depth data analysis. Traditional security mechanisms and models tailored to small-scale or isomorphic data are inadequate to securing social big data which exhibit enormous volume and diverse formats. Therefore, how to develop scalable cryptographic algorithms/protocols and lightweight data mining/organization/optimization models to solve the security and privacy challenges becomes crucial for the successful application of social big data.
About the Topics of Interest
Any topic related to security and privacy aspects, e.g., access control, authorization, authorization, and anonymization, for big data and social networks, will be considered. All aspects of design, theory and realization are of interest. The scope and interests for the special issue include but are not limited to the following list:
(i) Fundamentals and Technologies in Social Networks and Big Data
Social network models and platforms
Social network architectures and data models
Searching and discovery
Architectures for big data
Machine learning and deep learning
Scalable computing models, theories, and algorithms
Content analysis and data mining
Novel and incentive applications of social big data in various fields
Big data transformation, and presentation
Big data acquisition, integration, cleaning, and best practices
Large-scale data collection and filtering problem
Sparse data modeling, compressing, and sensing
(ii) Security and Privacy in Social Networks
Accountability and audit in social networks
Authentication and authorization in cloud services;
Secure access to social networks;
Big data privacy model in social networks
New trust mechanism in social networks
Privacy and security preserving protocol for social networks
Applications of cryptography in social networks
Secure data management in social networks;
Privacy modeling in social networks
Privacy-preserving social data publishing
Private information retrieval in social networks
Measurement studies of security & privacy issues in social networks
Combating cyber-crime: anti-phishing, anti-spam, anti-fraud techniques
(iii) Security and Privacy in Big Data
Access control models and anonymization algorithms in big data
Cryptography in big data and cloud computing
Data protection and integrity in big data
Secure searching in big data
Secure outsourcing computing in big data
System designs for secure data storage in big data
Security model and architecture for big data;
Software and system security for big data;
Scalability and auditing for big data;
Security and privacy in big data sharing and visualization;
Security and privacy in big data mining and analytics;
Data-centric security and data classification;
Privacy in big data applications and services;
Privacy in big data integration and transformation;
Privacy in big data storage management;
Threat detection using big data analytics;
Big data privacy policies and standards
(iv) System, Information and Network Security
High performance security systems
Secure system implementation
Database and system security
Secure operating systems
Cryptographic primitives and security protocols
Disaster recovery
Provable security
Key distribution and management
Intrusion detection and prevention
Privacy, anonymity and traceability
Identity management
Access controls and security mechanisms
Web & applications security
Secure routing and network management
Security in content delivery networks
Security in high speed network
Security in optical systems and networks
Network monitoring
Network security policies
------------------------------------------------------------------------
Important Dates
Submission deadline: December 31, 2018
First-roundpass notification (for a rejected paper): January 31, 2019
Acceptance/rejection notification: September 1, 2019
Publication materials due: December 31, 201
------------------------------------------------------------------------
Submission Format and Guideline
All submitted papers must be clearly written in excellent English and contain only original work, which has not been published by or is currently under review for any other journal or conference. Papers must not exceed 25 pages (one-column, at least 10pt fonts) including figures, tables, and references. A detailed submission guideline is available as ¡°Guide to Authors¡± at: https://www.elsevier.com/journals/journal-of-parallel-and-distributed-computing/0743-7315/guide-for-authors
All manuscripts and any supplementary material should be submitted through Elsevier Editorial System (EES). The authors must select as ¡°VSI: SP in Social Big Data¡± when they reach the ¡°Article Type¡± step in the submission process. The EES website is located at: http://evise.com/evise/jrnl/jpd
All papers will be peer-reviewed by at least three independent reviewers. Requests for additional information should be addressed to the guest editors.
------------------------------------------------------------------------
Guest Editors
Dr. Qin Liu¨C Corresponding Guest Editor
College of Computer Science and Electronic Engineering, Hunan University, China
Email: ***@.hnu.edu.cn; ***@126.com
Dr. Md Zakirul Alam Bhuiyan
Department of Computer and Information Sciences, Fordham University, USA
Email: ***@fordham.edu; ***@gmail.com
Dr. Jiankun Hu
School of Engineering and IT, University of New South Wales, Australia
Email: ***@adfa.edu.au
Dr. Jie Wu
Department of Computer and Information Sciences, Temple University, USA
Email: ***@temple.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
[Apologies for multiple postings]
https://www.journals.elsevier.com/journal-of-parallel-and-distributed-computing/call-for-papers/security-privacy-in-social-big-data
********************************************************************************************************************
Journal of Parallel and Distributed Computing
Security & Privacy in Social Big Data
------------------------------------------------------------------------
SCOPE of the SI
The rapid development of social networks dramatically changes the way people think, work, and interact. As more and more individual users proactively generate, share, and exchange digital contents through social media, social networks have become a key source of big data. However, with such vast interconnectivity, convergence of relationships, and shared user information comes increased security and privacy concerns in social big data. On one hand, users carelessly posting their personal information on social media which can easily have their privacy breached. On the other hand, malicious attackers may manipulate such information to make a profit.
There are two important security and privacy issues in social networks. The first is how to effectively utilize social data while protecting user privacy. The second is how to guarantee the authenticity of social data for an in-depth data analysis. Traditional security mechanisms and models tailored to small-scale or isomorphic data are inadequate to securing social big data which exhibit enormous volume and diverse formats. Therefore, how to develop scalable cryptographic algorithms/protocols and lightweight data mining/organization/optimization models to solve the security and privacy challenges becomes crucial for the successful application of social big data.
About the Topics of Interest
Any topic related to security and privacy aspects, e.g., access control, authorization, authorization, and anonymization, for big data and social networks, will be considered. All aspects of design, theory and realization are of interest. The scope and interests for the special issue include but are not limited to the following list:
(i) Fundamentals and Technologies in Social Networks and Big Data
Social network models and platforms
Social network architectures and data models
Searching and discovery
Architectures for big data
Machine learning and deep learning
Scalable computing models, theories, and algorithms
Content analysis and data mining
Novel and incentive applications of social big data in various fields
Big data transformation, and presentation
Big data acquisition, integration, cleaning, and best practices
Large-scale data collection and filtering problem
Sparse data modeling, compressing, and sensing
(ii) Security and Privacy in Social Networks
Accountability and audit in social networks
Authentication and authorization in cloud services;
Secure access to social networks;
Big data privacy model in social networks
New trust mechanism in social networks
Privacy and security preserving protocol for social networks
Applications of cryptography in social networks
Secure data management in social networks;
Privacy modeling in social networks
Privacy-preserving social data publishing
Private information retrieval in social networks
Measurement studies of security & privacy issues in social networks
Combating cyber-crime: anti-phishing, anti-spam, anti-fraud techniques
(iii) Security and Privacy in Big Data
Access control models and anonymization algorithms in big data
Cryptography in big data and cloud computing
Data protection and integrity in big data
Secure searching in big data
Secure outsourcing computing in big data
System designs for secure data storage in big data
Security model and architecture for big data;
Software and system security for big data;
Scalability and auditing for big data;
Security and privacy in big data sharing and visualization;
Security and privacy in big data mining and analytics;
Data-centric security and data classification;
Privacy in big data applications and services;
Privacy in big data integration and transformation;
Privacy in big data storage management;
Threat detection using big data analytics;
Big data privacy policies and standards
(iv) System, Information and Network Security
High performance security systems
Secure system implementation
Database and system security
Secure operating systems
Cryptographic primitives and security protocols
Disaster recovery
Provable security
Key distribution and management
Intrusion detection and prevention
Privacy, anonymity and traceability
Identity management
Access controls and security mechanisms
Web & applications security
Secure routing and network management
Security in content delivery networks
Security in high speed network
Security in optical systems and networks
Network monitoring
Network security policies
------------------------------------------------------------------------
Important Dates
Submission deadline: December 31, 2018
First-roundpass notification (for a rejected paper): January 31, 2019
Acceptance/rejection notification: September 1, 2019
Publication materials due: December 31, 201
------------------------------------------------------------------------
Submission Format and Guideline
All submitted papers must be clearly written in excellent English and contain only original work, which has not been published by or is currently under review for any other journal or conference. Papers must not exceed 25 pages (one-column, at least 10pt fonts) including figures, tables, and references. A detailed submission guideline is available as ¡°Guide to Authors¡± at: https://www.elsevier.com/journals/journal-of-parallel-and-distributed-computing/0743-7315/guide-for-authors
All manuscripts and any supplementary material should be submitted through Elsevier Editorial System (EES). The authors must select as ¡°VSI: SP in Social Big Data¡± when they reach the ¡°Article Type¡± step in the submission process. The EES website is located at: http://evise.com/evise/jrnl/jpd
All papers will be peer-reviewed by at least three independent reviewers. Requests for additional information should be addressed to the guest editors.
------------------------------------------------------------------------
Guest Editors
Dr. Qin Liu¨C Corresponding Guest Editor
College of Computer Science and Electronic Engineering, Hunan University, China
Email: ***@.hnu.edu.cn; ***@126.com
Dr. Md Zakirul Alam Bhuiyan
Department of Computer and Information Sciences, Fordham University, USA
Email: ***@fordham.edu; ***@gmail.com
Dr. Jiankun Hu
School of Engineering and IT, University of New South Wales, Australia
Email: ***@adfa.edu.au
Dr. Jie Wu
Department of Computer and Information Sciences, Temple University, USA
Email: ***@temple.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