Recently, it is very popular for users to outsource their big data from local devices to cloud servers since the cloud servers may provide huge storage ability and powerful computation capacity. Although the cloud server provides users with convenience, it is not always credible. When malicious users access to cloud servers, data on the server may expose users’ privacy. Even though normal visiting of outsourced data (such as visiting data for feature extraction) may reveal private information of users. In this way, the attacker can deduce or even recover part of the data by employing feature comparisons on derived features. As the most important process for data analysis, exploring secured data pre-processing over big data becomes of great importance.
This special issue will target on the recent progress of data pre-processing especially secured data processing, providing readers of this special issue with the state-of-the-art study on following topics (but not limited to):
- Data pre-processing with shallow methods
- Dimensionality reduction, missing data imputation, dictionary learning, data retrieval, and feature extraction
- Classification, regression, and clustering for big data
- Data pre-processing for multi-task or multi-view data
- Data pre-processing with deep learning
- Deep data pre-processing for big data
- Deep learning models with incomplete data
- Data pre-processing with deep transfer learning methods
- Deep data pre-processing with multi-view data
- Secured data pre-processing for big data
- Secured data pre-processing methods (including feature extraction, dimensionality reduction, missing data imputation, data retrieval, and dictionary learning)
- Secured deep data pre-processing techniques
- Secured techniques for classification, regression, and clustering
- Secured techniques for big healthcare data
Dr. Yanrong Guo, Hefei University of Technology, China (firstname.lastname@example.org) (Leader guest editor)
Dr. Kim Han Thung, University of North Carolina at Chapel Hill, NC, USA. (email@example.com)
Dr. Shichao Zhang, Central South University, China (firstname.lastname@example.org)
Paper submission deadline: 10th July 2020
First notification: 10th October 2020
Revision: 1st December 2020
Final decision: 1st February 2021
The submitted manuscripts should not be published or have been reviewing in any journals or conferences. The extension of a published conference paper should contain at most 30% duplication to the submitted version.
Papers should be prepared by following the instructions for authors of Neural Processing Letters at https://springer.com/11063, and the authors should submit their manuscript based on the following steps:
1. Submit manuscript on the submission website of Neural Processing Letters https://www.editorialmanager.com/nepl/default.aspx.
2. In the ‘Additional Information’ section, answer ‘Yes’ to the question ‘Does this manuscript belong to a special issue?’
3. Select ‘SI: Secured Big Data Pre-processing (SBDP)’.
The review process will be done by following the standard review process of this journal, where, in general, two reviewing rounds will have. After this, guest editors will make their initial decision and the EIC will send the final decision. In each round, each submission will be reviewed by at least three experts in the fields.