Privacy-Preserving Methods and Applications in Big Data Processing - Information Processing and Management Conference - Elsevier Search Support View Cart

Note: This special issue is a Thematic Track at IP&MC2022. For more information about IP&MC2022, please visit https://www.elsevier.com/events/conferences/information-processing-and-management-conference.

Title of the Special Issue

Privacy-Preserving Methods and Applications in Big Data Processing (VSI: IPMC2022 PRIVACY)

List of the Guest Editors

Dr. Zhen Zhu (Managing Guest Editor)Senior Lecturer in Big Data AnalyticsKent Business SchoolUniversity of Kent, Canterbury, United Kingdomz.zhu@kent.ac.uk

Prof. Qiujun LanProfessorBusiness SchoolHunan University, Changsha, Chinalanqiujun@hnu.edu.cn

Prof. Gang LiAssociate Professor, Deputy Director of D2i Research CentreSchool of Information TechnologyDeakin University, Burwood, Australiagang.li@deakin.edu.au

Prof. Vaidehi VijayakumarProfessor, Vice ChancellorMother Teresa Women’s University, Kodaikanal, Indiavicechancellor@motherteresawomenuniv.ac.in

Background

With the advances of Internet technology, the cyberspace generates a staggering volume of data, and the capacity to electronically store, transfer, and process those data continues to grow exponentially. Recently, Big Data has emerged as a new technology in response to this situation. However, data security and privacy issues are more challenging in the involved processes ranging from data collection, storing, processing, and analysis due to the enormous volume. As a result, innovative data security and data privacy preservation methods and applications are of paramount interest and importance to data services providers, businesses, regulators, and the general public.

This conference track (special issue) for IP&MC2022 aims to attract high-quality contributions from the community on the cutting edge theoretical and practical advancements related to data security and privacy preservation in Big Data processing. As the issues and challenges of privacy preservation are heterogeneous and constantly evolving, we welcome contributions across different domains (e.g., business, finance, health, mobility) and across different data natures (e.g., IoT, blockchain, social media, and networks).

Possible Topics of Submissions

The suggested topics include but are not limited to:

Important Dates

Submission Guidelines

Submit your manuscript to the Special Issue category (VSI: IPMC2022 PRIVACY) through the online submission system of Information Processing & Management. https://www.editorialmanager.com/ipm/

Authors will prepare the submission following the Guide for Authors on IP&M journal at (https://www.elsevier.com/journals/information-processing-and-management/0306-4573/guide-for-authors). All papers will be peer-reviewed following the IP&MC2022 reviewing procedures.

The authors of accepted papers will be obligated to participate in IP&MC2022 and present the paper to the community to receive feedback. The accepted papers will be invited for revision after receiving feedback on the IP&MC 2022 conference. The submissions will be given premium handling at IP&M following its peer-review procedure and, (if accepted), published in IP&M as full journal articles, with also an option for a short conference version at IP&MC2022.

Please see this infographic for the manuscript flow:https://www.elsevier.com/__data/assets/pdf_file/0003/1211934/IPMC2022Timeline10Oct2022.pdf

For more information about IP&MC2022, please visit https://www.elsevier.com/events/conferences/information-processing-and-management-conference.

References

Chen, J., Liu, G. and Liu, Y., 2020. Lightweight privacy-preserving raw data publishing scheme. IEEE Transactions on Emerging Topics in Computing.

Cronin, B., Perra, N., Rocha, L.E.C., Zhu, Z., Pallotti, F., Gorgoni, S., Conaldi, G. and De Vita, R., 2021. Ethical implications of network data in business and management settings. Social Networks, 67, pp.29-40.

Wang, Q., Zhang, Y., Lu, X., Wang, Z., Qin, Z. and Ren, K., 2016. Real-time and spatio-temporal crowd-sourced social network data publishing with differential privacy. IEEE Transactions on Dependable and Secure Computing, 15(4), pp.591-606.

Zhu, T., Li, G., Zhou, W. and Philip, S.Y., 2017. Differentially private data publishing and analysis: A survey. IEEE Transactions on Knowledge and Data Engineering, 29(8), pp.1619-1638.