AQ-DP: A New Differential Privacy Scheme Based on Quasi-Identifier Classifying in Big Data

Ke, H; Fu, A; Yu, S; Chen, S

AQ-DP: A New Differential Privacy Scheme Based on Quasi-Identifier Classifying in Big Data

Ke, H Fu, A Yu, S

Chen, S

Permalink

Publication Type:: Conference Proceeding
Citation:: 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings, 2018
Issue Date:: 2018-01-01

Closed Access

	Filename	Description	Size
	AQ-DP A New Differential Privacy Scheme Based on Quasi-Identifier Classifying in Big Data.pdf	Published version	571.06 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Ke, H	en_US
dc.contributor.author	Fu, A	en_US
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743	en_US
dc.contributor.author	Chen, S	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings, 2018	en_US
dc.identifier.isbn	9781538647271	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134522
dc.description.abstract	© 2018 IEEE. The rapid development of big data has brought great convenience to human's lives. The circulation and sharing of information are two main characteristics of the big data era. However, the risk of privacy leakage is also greatly increased when we enjoy the various services of big data. Therefore, how to protect the data privacy in the complex context of big data has become a research hotspot in academic circles. Most of the current researches on privacy protection are divided into two research fields: k-anonymity and differential privacy. Some existing research shows that traditional methods of privacy protection, such as k-anonymity and its extension, cannot achieve absolutely security. The emergence of differential privacy provides a new solution for privacy protection. We draw the lessons from exiting work and propose a new privacy method based on differential privacy: AQ-DP. We propose the first method for classifying quasi-identifiers based on sensitive attributes, which divide quasi-identifiers into associated quasi-identifiers (AQI) and non-associated quasi-identifiers (NAQI). The purpose is not to lose the correlation between quasi-identifiers and sensitive attributes. Our model AQ-DP carries out random shuffling of NAQls., generalizes the AQIs., and adds random noise that satisfies the laplacian distribution to the statistics. We have conducted extensive experiments, confirming that our model can achieve a satisfying privacy level and data utility.	en_US
dc.relation.ispartof	2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings	en_US
dc.relation.isbasedon	10.1109/GLOCOM.2018.8647941	en_US
dc.title	AQ-DP: A New Differential Privacy Scheme Based on Quasi-Identifier Classifying in Big Data	en_US
dc.type	Conference Proceeding
utslib.for	0805 Distributed Computing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2018 IEEE. The rapid development of big data has brought great convenience to human's lives. The circulation and sharing of information are two main characteristics of the big data era. However, the risk of privacy leakage is also greatly increased when we enjoy the various services of big data. Therefore, how to protect the data privacy in the complex context of big data has become a research hotspot in academic circles. Most of the current researches on privacy protection are divided into two research fields: k-anonymity and differential privacy. Some existing research shows that traditional methods of privacy protection, such as k-anonymity and its extension, cannot achieve absolutely security. The emergence of differential privacy provides a new solution for privacy protection. We draw the lessons from exiting work and propose a new privacy method based on differential privacy: AQ-DP. We propose the first method for classifying quasi-identifiers based on sensitive attributes, which divide quasi-identifiers into associated quasi-identifiers (AQI) and non-associated quasi-identifiers (NAQI). The purpose is not to lose the correlation between quasi-identifiers and sensitive attributes. Our model AQ-DP carries out random shuffling of NAQls., generalizes the AQIs., and adds random noise that satisfies the laplacian distribution to the statistics. We have conducted extensive experiments, confirming that our model can achieve a satisfying privacy level and data utility.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/134522