Slicing-Based Enhanced Method for Privacy-Preserving in Publishing Big Data  

作　　者：Mohammed BinJubier Mohd Arfian Ismail Abdulghani Ali Ahmed Ali Safaa Sadiq 

机构地区：[1]Faculty of Computing,Universiti Malaysia Pahang,Kuantan,Pahang,Malaysia [2]School of Computer Science and Informatics,De Montfort University,Leicester,LE19BH,United Kingdom [3]School of Engineering,Computing and Mathematical Sciences,University of Wolverhampton,Wulfruna Street Wolverhampton,WV11LY,United Kingdom

出　　处：《Computers, Materials & Continua》2022年第8期3665-3686,共22页计算机、材料和连续体（英文）

基　　金：This work was supported by Postgraduate Research Grants Scheme(PGRS)with Grant No.PGRS190360.

摘　　要：Publishing big data and making it accessible to researchers is important for knowledge building as it helps in applying highly efficient methods to plan,conduct,and assess scientific research.However,publishing and processing big data poses a privacy concern related to protecting individuals’sensitive information while maintaining the usability of the published data.Several anonymization methods,such as slicing and merging,have been designed as solutions to the privacy concerns for publishing big data.However,the major drawback of merging and slicing is the random permutation procedure,which does not always guarantee complete protection against attribute or membership disclosure.Moreover,merging procedures may generatemany fake tuples,leading to a loss of data utility and subsequent erroneous knowledge extraction.This study therefore proposes a slicingbased enhanced method for privacy-preserving big data publishing while maintaining the data utility.In particular,the proposed method distributes the data into horizontal and vertical partitions.The lower and upper protection levels are then used to identify the unique and identical attributes’values.The unique and identical attributes are swapped to ensure the published big data is protected from disclosure risks.The outcome of the experiments demonstrates that the proposed method could maintain data utility and provide stronger privacy preservation.

关 键 词：Big data big data privacy preservation ANONYMIZATION data publishing 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]