A hybrid approach to prevent composition attacks for independent data releases
Jiuyong Li,Muzammil M. Baig,A.H.M. Sarowar Sattar,Xiaofeng Ding,Jixue Liu,Millist W. Vincent +5 more
TLDR
A hybrid algorithm, which combines sampling, perturbation and generalization to protect data privacy from composition attacks is proposed and experimentally demonstrates that the proposed anonymization technique significantly reduces the risk of composition attacks and also preserves good data utility.About:
This article is published in Information Sciences.The article was published on 2016-11-01 and is currently open access. It has received 26 citations till now. The article focuses on the topics: Data anonymization & Information privacy.read more
Citations
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Journal ArticleDOI
A New Approach to Privacy-Preserving Multiple Independent Data Publishing
TL;DR: This work proposes an innovative approach to protecting published datasets from composition attack by using a cell generalization approach, which can preserve more data utility than the existing methods.
Journal ArticleDOI
An Effective Grouping Method for Privacy-Preserving Bike Sharing Data Publishing
TL;DR: Experimental results show that the proposed grouping based anonymization method can protect user privacy in the released datasets from disclosure risks and can keep more data utility compared with existing methods.
Journal ArticleDOI
Privacy preserving serial publication of transactional data
TL;DR: This paper develops a rigorous privacy guarantee and a serial publication method Sanony that satisfies the privacy guarantee without excessive utility loss and shows the framework affords stronger privacy with much lower perturbation rates than existing state-of-the-art techniques.
Proceedings ArticleDOI
A General Framework for Privacy Preserving Sequential Data Publishing
TL;DR: Experimental results show that the proposed framework counter the published dataset from linking attack and keep more data utility than the existing methods.
Journal ArticleDOI
Composition attack against social network data
TL;DR: A new algorithm for the composition attack is proposed and its usability is demonstrated with experiments using pairs of synthetic scale-free networks substituting real social networks.
References
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Journal ArticleDOI
k -anonymity: a model for protecting privacy
TL;DR: The solution provided in this paper includes a formal protection model named k-anonymity and a set of accompanying policies for deployment and examines re-identification attacks that can be realized on releases that adhere to k- anonymity unless accompanying policies are respected.
Book ChapterDOI
Calibrating noise to sensitivity in private data analysis
TL;DR: In this article, the authors show that for several particular applications substantially less noise is needed than was previously understood to be the case, and also show the separation results showing the increased value of interactive sanitization mechanisms over non-interactive.
Book ChapterDOI
Differential privacy
TL;DR: In this article, the authors give a general impossibility result showing that a formalization of Dalenius' goal along the lines of semantic security cannot be achieved, and suggest a new measure, differential privacy, which, intuitively, captures the increased risk to one's privacy incurred by participating in a database.
Journal ArticleDOI
L-diversity: Privacy beyond k-anonymity
TL;DR: This paper shows with two simple attacks that a \kappa-anonymized dataset has some subtle, but severe privacy problems, and proposes a novel and powerful privacy definition called \ell-diversity, which is practical and can be implemented efficiently.