An Improved Algorithm of Individuation K-Anonymity for Multiple Sensitive Attributes

Zhang, Lin; Xuan, Jie; Si, Ruo-Qian; Wang, Ruchuan

doi:10.1007/s11277-016-3922-4

Cited by 23 publications

(15 citation statements)

References 13 publications

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“…For evaluation we used a measurement metrics tool which is called discernibility metrics [32,33]. Discernibility metrics measures number of tuples that cannot be distinguished to others, this metrics also measures penalty point to generalized tuples.…”

Section: Discussionmentioning

confidence: 99%

Privacy Preserving Data Publishing with Multiple Sensitive Attributes based on Overlapped Slicing

2019

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Investigation into privacy preserving data publishing with multiple sensitive attributes is performed to reduce probability of adversaries to guess the sensitive values. Masking the sensitive values is usually performed by anonymizing data by using generalization and suppression techniques. A successful anonymization technique should reduce information loss due to the generalization and suppression. This research attempts to solve both problems in microdata with multiple sensitive attributes. We propose a novel overlapped slicing method for privacy preserving data publishing with multiple sensitive attributes. We used discernibility metrics to measure information loss. The experiment result shows that our method obtained a lower discernibility value than other methods.Information 2019, 10, 362 2 of 18 data publishing based on single sensitive attribute, while in the real world, it should be a multiple sensitive attributes problem. Some researches concerned with multiple sensitive attributes, but almost all of them only solved partial problems. When, k-anonymity, l-diversity, and p-sensitive are performed by anonymizing data, it tends to produce information loss. While we use anatomy [8], a model, which dissociates quasi identifier attributes and sensitive attributes, it produces minimum information loss.The major problem of previous works is when a privacy model is applied to a microdata using any methods, it always produces information loss values. This work tried to minimize the information loss when anonymizing is conducted. This investigation started by distributing sensitive values. The sensitive values are distributed evenly in each bucket, which represents equivalence class. We also employed overlapped slicing not only for keeping the relationship between quasi identifier attributes and sensitive attributes, but also for scrambling the records to hold its privacy. It means that this work also aims to maintain its privacy but obtains better data utility. This paper has some following contributions:

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Section: Discussionmentioning

confidence: 99%

Privacy Preserving Data Publishing with Multiple Sensitive Attributes based on Overlapped Slicing

2019

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“…Ahmed et al [16] proposed a probabilistic model of multiple sensitive attribute diversity to prevent identification or non-membership attack that arises when the microdata with multiple sensitive attributes is published. In [17][18][19], a (α, l) model was applied to satisfy the diversity requirements for multiple sensitive attributes. Zhang et al [17] used anatomization with generalization and suppression based on the (α, l) model.…”

Section: Related Workmentioning

confidence: 99%

“…In [17][18][19], a (α, l) model was applied to satisfy the diversity requirements for multiple sensitive attributes. Zhang et al [17] used anatomization with generalization and suppression based on the (α, l) model. Guo et al [18] proposed a personalized privacy preserving model for multiple sensitive attributes based on MSB, called personalized (α, l)-anonymity model.…”

Section: Related Workmentioning

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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“…ϵ-differential privacy [27] is an example of the semantic data model. e researchers have proposed both the syntactic and semantic privacy models for different types of data, e.g., single sensitive attribute [3,4,6], or MSAs [7][8][9], or 1 : m (i.e., one individual having many records) [28] microdata. For preserving the privacy, the algorithms in privacy models practice different approaches.…”

Section: Data Privacy Models Andmentioning

confidence: 99%

“…To ensure privacy of such information, most of the existing algorithms [1][2][3][4][5][6] in the literature deal exist with a single sensitive attribute only. However, a dataset may practically have multiple sensitive attributes (MSAs) [7][8][9][10][11][12][13][14]. For example, a hospital may publish data with more than one sensitive attribute, such as disease, symptom, and physician as shown in Table 1.…”

Section: Introductionmentioning

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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An Improved Algorithm of Individuation K-Anonymity for Multiple Sensitive Attributes

Cited by 23 publications

References 13 publications

Privacy Preserving Data Publishing with Multiple Sensitive Attributes based on Overlapped Slicing

Privacy Preserving Data Publishing with Multiple Sensitive Attributes based on Overlapped Slicing

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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