Semantic Tree Based PPDP Technique for Multiple Sensitive Attributes in Inter Cloud

Digital devices and information systems have made data privacy essential. The collected data contains sensitive attributes such as salary, marital status and health history that need to be protected. Such data is exchanged or published to a third party using cloud infrastructure to perform various a...

Full description

Saved in:
Bibliographic Details
Published in:SN computer science 2024-07, Vol.5 (6), p.722, Article 722
Main Authors: Gadad, Veena, Sowmyarani, C. N., Dayananda, P.
Format: Article
Language:English
Subjects:
Advanced Computing and Data Sciences
Algorithms
Bronchitis
Cardiovascular disease
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Data analysis
Data collection
Data Structures and Information Theory
Datasets
Gender
Health care
Heart
Information systems
Information Systems and Communication Service
Original Research
Pattern Recognition and Graphics
Privacy
Publishing
Residues
Semantics
Software Engineering/Programming and Operating Systems
Vision
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Digital devices and information systems have made data privacy essential. The collected data contains sensitive attributes such as salary, marital status and health history that need to be protected. Such data is exchanged or published to a third party using cloud infrastructure to perform various analyses, conduct research, and make critical decisions. Unauthorized users of the published data may violate privacy, notwithstanding the benefits. Data anonymization is one of the technique for achieving data privacy. Existing techniques consider single sensitive attribute and data is anonymized using generalization or suppression approaches. On observation, it is found that these techniques are less efficient since the collected data contains multiple sensitive attributes when anonymized using the same approaches leads to higher information loss and residue records. In this paper, multiple sensitive attributes are considered and the dataset is anonymized by constructing a semantic hierarchical tree it is further partitioned using the anatomy approach. Later, the partitions are stored in interclouds to achieve better privacy protection. Experiments are conducted to observe and analyze the computational performance, residue records and diversity percentage. The results obtained prove that the proposed technique is efficient when compared to the existing ones.
ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-024-03079-7