Call for Papers on Privacy Preserving Techniques for Big Data Analytics: Challenges and Research Directions
Information Technology and Management is seeking submissions to a forthcoming Special Issue on Privacy Preserving Techniques for Big Data Analytics: Challenges and Research Directions.
- Privacy Preserving Techniques for Big Data Analytics: Challenges and Research Directions
Closes January 10, 2022
Technological development makes human work more accessible in recent days by connecting people and devices, resulting in managing, storing and analyzing a massive amount of data. Using big data analytics is vital to manage these huge volumes of data where it is characterized by 4Vs, velocity, volume, variety, and veracity. Here, the veracity or trustworthiness of data is the biggest challenge in big data analytics. Privacy issues prevail in all dimensions of utilizing data, such as collecting, processing, interpreting, and managing. Many privacy-related threats are arising regularly, like eavesdropping, losing confidentiality, mining, and cyber-attacks. Hence, safeguarding privacy is needed during three essential tasks: data dissemination, reconnoitering, and data analysis. Further, implementing enhanced strategies such as de-identification, aggregation, encryption, randomization, anonymisation, perturbation, and others could improve the analytical process more secure.
Apart from benefits, several challenges still exist in using these techniques. Since the data has multiple features and generic characteristics, the de-identification approach does not guarantee private data. Next, the aggregation method losses its sheen because the encryption of data is a complex process, yet it will preserve the actual meaning of the raw data. Along with it, voluminous data stored increases computational complexity and processing time. Therefore, various privacy-preserving techniques like suppression, perturbation, homomorphic encryption fail to produce the desired privacy and security for sensitive data. For this reason, before applying any privacy-preserving technique, the following factors, such as efficacy, robustness, computational efficiency, and complexity of data, should be analyzed and understood. In addition to this, another important metric that helps to understand the privacy technique is utilized. It can be helpful for both general and specific purposes, structured and unstructured. However, challenges prevail in differentiating sensitive and non-sensitive attributes of unstructured data. Despite using all these privacy-preserving techniques, privacy issues do exist. To overcome this, researchers in future can concentrate on quantifying vulnerability, horizontal-vertical partitioning, map hybrid data governance, association rule mining to improve confidentiality and integrity. This special issue provides an opportunity to researchers to discuss their ideologies related to privacy preserving techniques for big data analytics.
The topics of interest for the special issue:
- Analyzing data breaches, brokerage, and discrimination for ensuring big data privacy in organizational structure
- Strategies for real-time monitoring of big data to protect customer data privacy
- Critical evaluation of different privacy-preserving paradigms for big data analytics: An enterprise perspective
- Emerging tools and techniques to overcome challenges in preserving useful business information using big data analytics
- Evolution of flexible and efficient techniques to solve de-identification risks in big data based on e-commerce environment
- Privacy-preserving of extensive unstructured data in financial departments: Solutions and Challenges
- Novel research directions efficacy on privacy-preserving techniques of big data models in Management Information System
- Role of generic privacy-preserving mechanisms for internal and external transaction processing systems in big data
- Implementing the concept of privacy by design for big data-based business settings
- Future of model-oriented paradigms to ensure big data privacy for analysis, and visualization of information in an organization
We expect full-length submissions with a sufficient level of rigor consistent with the high standard of the journal. The submission can use any appropriate method to analyze problems: analysis of data, mathematical analysis, game theories, etc. The authors should try to keep the papers to be no longer than 38 pages double-spaced in a font size of 11 and in Word or PDF format. Please follow the detailed submission guidelines provided at https://www.springer.com/journal/10799/submission-guidelines. When answering submission questions, you will specify that your submission is for this special issue.
First-round decision: January 10, 2022
Deadline for Revised Papers: June 06, 2022
Final Acceptance: August 28, 2022
Hassan Fouad Mohamed El-Sayed
Helwan University, Egypt
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