Journal of Signal Processing Systems - Big Data Security Track
We are excited to announce a new Big Data Security track in the Journal of Signal Processing Systems, to be handled by Area Editor, Dr. Meikang Qiu of Texas A&M University Commerce, USA. Articles accepted to the track will be published on a rolling basis and included in a designated collection (this opens in a new tab)online.
Aim:
The era of data deluge has brought an unprecedented amount of data being produced, gathered, used, and stored in numerous ways. One of the most popular research topics in this realm is obtaining additional benefits from the data deluge, as well as using the outcomes to increase privacy protections. However, it is also a great challenge to run a giant size of data analytics by using a central processor and storage. A distributed working mode using parallel data processing is an alternative approach for data value acquisitions, by which new privacy issues are introduced as well. Multiple factors need to be considered when an effective mechanism/strategy of big data analytics is formed, which include high performance, privacy, and other constraints, such as energy and power. Current solutions are insufficient or un-scalable to deal with dramatically big volumes of data. Therefore, exploring new approaches and collecting state-of-the-art techniques in big data analytics and privacy protections have an urgent demand in both academics and industries. This track aims to solicit both academic research and professional contributions focusing on advanced techniques and explorations in big data analytics and privacy protections, which covers all related implementations, mechanisms, model, framework, case studies, and empirical studies.
Paper Submission:
Authors need to directly log into the JSPS submission system and find the article type: “Big Data Security Track”. Submissions are open now: https://www.editorialmanager.com/vlsi/default.aspx (this opens in a new tab)
Scope:
Topics of particular interest include, but are not limited to:
- Scalable data analytics
- Big data analytics techniques and models
- Deep data analytics mechanisms
- In-memory mass data analytics
- Storing, dropping and filtering data
- Relevant/redundant/obsolete data analytics
- Volume vs. semantics analytics
- Nomad analytics
- Predictive analytics
- Trust management in data analytics
- Legal issues analytics
- Failure on data analytics
- Analytics visualization
- Multi-modal support for data analytics
- Big Data platforms
- Big Data persistence and preservation
- Big Data and social networks
- Big Data economics
- High-performance data analytics
- Compressive sampling, matrix completion, low-rank models, and dimensionality reduction
- Efficient learning and clustering
- Robustness to outliers; convergence and complexity issues; performance analysis
- Privacy protection algorithm for cloud- based big data
- Embedded systems security
- Forensics & Hardware security
- Intrusion detection & protocol security
- Malware and unwanted software
- Mobile and Web security and privacy
- Language-based security
- Network and systems security
- Privacy technologies and mechanisms
- Access control and authorization
- Accountability, Anonymity
- Application security, Attacks and defenses
Submission guidelines
Papers must be prepared in accordance with the Journal guidelines: https://www.springer.com/journal/11265/submission-guidelines (this opens in a new tab)
Submitted papers should present original, unpublished work, relevant to one of the topics of the Security Track. All submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least three independent reviewers. It is the policy of the journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the review process. Manuscripts will be subject to a peer reviewing process and must conform to the author guide lines available on the JSPS website at: https://www.springer.com/11265
Author Resources
Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by other journals. Springer provides a host of information about publishing in a Springer Journal on our Journal Author Resources (this opens in a new tab) page, including FAQs (this opens in a new tab), Tutorials (this opens in a new tab) along with Help and Support. (this opens in a new tab)
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