Privacy Protection Method for Multiple Sensitive Attributes Based on Strong Rule

Yi, Tong; Shi, Minjia

doi:10.1155/2015/464731

Cited by 16 publications

(19 citation statements)

References 12 publications

(38 reference statements)

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“…Maintaining anonymity against datasets with multiple sensitive attributes is an important and practical problem as we cannot always go with an assumption that datasets contain only one sensitive attribute. Although good progress on some scenarios have been made in [10,11,12,13,14,16,18,19,20] and this paper, the problem still at large remains open and challenging. All these paper have addressed the problem of multiple sensitive attributes but not in the context of personalization.…”

Section: Discussionmentioning

confidence: 97%

“…Yi,T. and Shi,M [18] presented that an attack method uses the association rules to get the users' privacy and accordingly presented a protection model. Through theoretical and experimental analysis, the authors proved that the new protection model can provide better protection for privacy, and it was able to preserve useful relationships in released tables.…”

Section: Related Workmentioning

confidence: 99%

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A Novel Approach for Personalized Privacy Preserving Data Publishing with Multiple Sensitive Attributes

Reddy¹,

Raju²,

Kumari³

2018

IJET

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The Personalized Privacy has drawn a lot of attention from diverse magnitudes of the public and various functional units like bureau of statistics, and hospitals. A large number of data publishing models and methods have been proposed and most of them focused on single sensitive attribute. A few research papers marked the need for preserving privacy of data consisting of multiple sensitive attributes. Applying the existing methods such as k-anonymity, l-diversity directly for publishing multiple sensitive attributes would minimize the utility of the data. Moreover, personalization has not been studied in this dimension. In this paper, we present a publishing model that manages personalization for publishing data with multiple sensitive attributes. The model uses slicing technique supported by deterministic anonymization for quasi identifiers; generalization for categorical sensitive attributes; and fuzzy approach for numerical sensitive attributes based on diversity. We cap the belief of an adversary inferring a sensitive value in a published data set to as high as that of an inference based on public knowledge. The experiments were carried out on census dataset and synthetic datasets. The results ensure that the privacy is being safeguarded without any compromise on the utility of the data.

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Section: Discussionmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

A Novel Approach for Personalized Privacy Preserving Data Publishing with Multiple Sensitive Attributes

Reddy¹,

Raju²,

Kumari³

2018

IJET

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“…But the rating technique can be attacked by applying association rules due to the relationship between sensitive attribute values. Yi et al [47] removed the weaknesses of the rating technique and eliminated the threat of association attack.…”

Section: Related Workmentioning

confidence: 99%

“…The above methods for multiple sensitive attributes do not consider the sensitive requirements of various sensitive attributes. Different sensitive attributes may have different sensitivity requirements, so the rating techniques for multiple sensitive attributes were introduced [46,47]. These rating techniques not only protect privacy for multiple sensitive attributes, but also keep a large amount of correlations of the microdata.…”

Section: Introductionmentioning

confidence: 99%

Privacy Preserving Data Publishing for Multiple Sensitive Attributes Based on Security Level

Xiao

2020

Information

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Privacy preserving data publishing has received considerable attention for publishing useful information while preserving data privacy. The existing privacy preserving data publishing methods for multiple sensitive attributes do not consider the situation that different values of a sensitive attribute may have different sensitivity requirements. To solve this problem, we defined three security levels for different sensitive attribute values that have different sensitivity requirements, and given an L s l -diversity model for multiple sensitive attributes. Following this, we proposed three specific greed algorithms based on the maximal-bucket first (MBF), maximal single-dimension-capacity first (MSDCF) and maximal multi-dimension-capacity first (MMDCF) algorithms and the maximal security-level first (MSLF) greed policy, named as MBF based on MSLF (MBF-MSLF), MSDCF based on MSLF (MSDCF-MSLF) and MMDCF based on MSLF (MMDCF-MSLF), to implement the L s l -diversity model for multiple sensitive attributes. The experimental results show that the three algorithms can greatly reduce the information loss of the published microdata, but their runtime is only a small increase, and their information loss tends to be stable with the increasing of data volume. And they can solve the problem that the information loss of MBF, MSDCF and MMDCF increases greatly with the increasing of sensitive attribute number.

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“…An adversary can use related bk to breach the privacy. e authors in [32] prevented the data from association attack and removed the weakness of the rating algorithm. In [37,38], the authors perform vertical partitioning (i.e., anatomy) and implement decomposition and decomposition plus, respectively, to achieve l-diversity for MSAs.…”

Section: Syntactic Anonymization Literature For Multiple Sensitive Atmentioning

confidence: 99%

Privacy Preserving for Multiple Sensitive Attributes against Fingerprint Correlation Attack Satisfying c-Diversity

Khan

Tao

Anjum

et al. 2020

Wireless Communications and Mobile Computing

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Privacy preserving data publishing (PPDP) refers to the releasing of anonymized data for the purpose of research and analysis. A considerable amount of research work exists for the publication of data, having a single sensitive attribute. The practical scenarios in PPDP with multiple sensitive attributes (MSAs) have not yet attracted much attention of researchers. Although a recently proposed technique (p, k)-Angelization provided a novel solution, in this regard, where one-to-one correspondence between the buckets in the generalized table (GT) and the sensitive table (ST) has been used. However, we have investigated a possibility of privacy leakage through MSA correlation among linkable sensitive buckets and named it as “fingerprint correlation fcorr attack.” Mitigating that in this paper, we propose an improved solution “c,k-anonymization” algorithm. The proposed solution thwarts the fcorr attack using some privacy measures and improves the one-to-one correspondence to one-to-many correspondence between the buckets in GT and ST which further reduces the privacy risk with increased utility in GT. We have formally modelled and analysed the attack and the proposed solution. Experiments on the real-world datasets prove the outperformance of the proposed solution as compared to its counterpart.

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Privacy Protection Method for Multiple Sensitive Attributes Based on Strong Rule

Cited by 16 publications

References 12 publications

A Novel Approach for Personalized Privacy Preserving Data Publishing with Multiple Sensitive Attributes

A Novel Approach for Personalized Privacy Preserving Data Publishing with Multiple Sensitive Attributes

Privacy Preserving Data Publishing for Multiple Sensitive Attributes Based on Security Level

Privacy Preserving for Multiple Sensitive Attributes against Fingerprint Correlation Attack Satisfying c-Diversity

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