Hierarchical anonymization algorithms against background knowledge attack in data releasing

Amiri, Fatemeh; Yazdani, Nasser; Shakery, Azadeh; Chinaei, Amir H.

doi:10.1016/j.knosys.2016.03.004

Cited by 39 publications

(39 citation statements)

References 17 publications

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“…We present the detailed overview of the five datasets that were used in the experiments in Table 1. The first three datasets are publicly available [74,75], and the last two were created synthetically by respecting the attributes values distribution and percentage in real SN [76]. We assigned two SA (political views and online disease community affiliation) with distinct values to synthetically created datasets.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing data

Majeed

Lee

2020

Appl Intell

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User attributes affect community (i.e., a group of people with some common properties/attributes) privacy in users' data publishing because some attributes may expose multiple users' identities and their associated sensitive information during published data analysis. User attributes such as gender, age, and race, may allow an adversary to form users' communities based on their values, and launch sensitive information inference attack subsequently. As a result, explicit disclosure of private information of a specific users' community can occur from the privacy preserved published data. Each item of user attributes impacts users' community privacy differently, and some types of attributes are highly susceptible. More susceptible types of attributes enable multiple users' unique identifications and sensitive information inferences more easily, and their presence in published data increases users' community privacy risks. Most of the existing privacy models ignore the impact of susceptible attributes on user's community privacy and they mainly focus on preserving the individual privacy in the released data. This paper presents a novel data anonymization algorithm that significantly improves users' community privacy without sacrificing the guarantees on anonymous data utility in publishing data. The proposed algorithm quantifies the susceptibility of each attribute present in user's dataset to effectively preserve users' community privacy. Data generalization is performed adaptively by considering both user attributes' susceptibility and entropy simultaneously. The proposed algorithm controls overgeneralization of the data to enhance anonymous data utility for the legitimate information consumers. Due to the widespread applications of social networks (SNs), we focused on the SN users' community privacy preserved and utility enhanced anonymous data publishing. The simulation results obtained from extensive experiments, and comparisons with the existing algorithms show the effectiveness of the proposed algorithm and verify the aforementioned claims.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing data

Majeed

Lee

2020

Appl Intell

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“…For preserving the privacy, the algorithms in privacy models practice different approaches. ese approaches can be categorized to (i) generalization [1][2][3][4][5][13][14][15] (i.e., greedily convert the more specialized values to less specialized values), (ii) anatomy [25,26] (i.e., partition the QI and S attributes), and (iii) microaggregation [29,30] (i.e., dataset is partitioned into clusters where QI values of records are replaced with the mean of value). e proposed work in this paper considers the syntactic data privacy, using generalization and anatomy for MSAs.…”

Section: Data Privacy Models Andmentioning

confidence: 99%

“…Different general purpose posteriori measures for utility and privacy loss [9,15,18,22] are available for generalization-based algorithms. In these approaches, the publisher does not know about the recipient analysing method.…”

Section: Experimental Analysismentioning

confidence: 99%

“…erefore, data should be anonymized in such a way that the research analysts may extract useful information. Balancing privacy and utility in privacy preserving data publishing (PPDP) is a NP-hard problem [15][16][17][18][19][20]. erefore, the scenario in this paper is more challenging, as we consider the dimensionality in quasi-identifiers (QIs) as well as more than one sensitive attributes, i.e., MSAs.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Specifically, most of the existing techniques assess the performance using traditional metrics only. Unlike those, the information loss is measured in terms of traditional metrics such as global certainty penalty [4,24], non-uniform entropy metric [25], normalized information loss [26,27], normalized certainty penalty [4], query error [11,28], sum of squared errors [29]. But, in proposed approach, Nayahi and Kavitha worked best to succeed in the anonymization using centroid-based replacement of QID values that is computationally superior to suppression in terms of information loss and less expensive than generalization.…”

Section: For Utility Preserving Data Clusteringmentioning

confidence: 99%

Data Privacy Protection Mechanisms in Cloud

Singh

2017

Data Sci. Eng.

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In the cloud computing environment, the privacy of the electronic data is a serious issue that requires special considerations. We have presented a state-of-the-art review of the methodologies and approaches that are currently being used to cope with the significant issue of privacy. We have categorized the privacy-preserving approaches into four categories, i.e., privacy by cryptography, privacy by probability, privacy by anonymization and privacy by ranking. Moreover, we have developed taxonomy of the techniques that have been used to preserve the privacy of the governing data. We also presented a comprehensive comparison of the privacy-preserving approaches from the angle of the privacy-preserving requirements' satisfaction. Therefore, it is highly desirable that the mechanisms should be developed to deploy efficient auditing and accountability mechanisms that anonymously monitor the utilization of data records and track the provenance to ensure the confidentiality of the data.

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Hierarchical anonymization algorithms against background knowledge attack in data releasing

Cited by 39 publications

References 17 publications

Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing data

Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing data

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

Data Privacy Protection Mechanisms in Cloud

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