An efficient approach for publishing microdata for multiple sensitive attributes

The publication of microdata is pivotal for medical research purposes, data analysis and data mining. These published data contain a substantial amount of sensitive information, for example, a hospital may publish many sensitive attributes such as diseases, treatments and symptoms. The release of mu...

Full description

Saved in:
Bibliographic Details
Published inThe Journal of supercomputing Vol. 74; no. 10; pp. 5127 - 5155
Main Authors Anjum, Adeel, Ahmad, Naveed, Malik, Saif U. R., Zubair, Samiya, Shahzad, Basit
Format Journal Article
LanguageEnglish
Published New York Springer US 01.10.2018
Springer Nature B.V
Subjects
Compilers
Computer Science
Data analysis
Data mining
Interpreters
Medical research
Privacy
Processor Architectures
Programming Languages
DCP
DE
ST
SBT
Angelization
(
KL-divergence
SLOMS
MSA
SLAMSA
GT
Online AccessGet full text

Cover

More Information
Summary:The publication of microdata is pivotal for medical research purposes, data analysis and data mining. These published data contain a substantial amount of sensitive information, for example, a hospital may publish many sensitive attributes such as diseases, treatments and symptoms. The release of multiple sensitive attributes is not desirable because it puts the privacy of individuals at risk. The main vulnerability of such approach while releasing data is that if an adversary is successful in identifying a single sensitive attribute, then other sensitive attributes can be identified by co-relation. A whole variety of techniques such as SLOMS, SLAMSA and others already exist for the anonymization of multiple sensitive attributes; however, these techniques have their drawbacks when it comes to preserving privacy and ensuring data utility. The extant framework lacks in terms of preserving privacy for multiple sensitive attributes and ensuring data utility. We propose an efficient approach ( p, k )-Angelization for the anonymization of multiple sensitive attributes. Our proposed approach protects the privacy of the individuals and yields promising results compared with currently used techniques in terms of utility. The ( p, k )-Angelization approach not only preserves the privacy by eliminating the threat of background join and non-membership attacks but also reduces the information loss thus improving the utility of the released information.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-018-2390-x