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

Xiao, Yuelei; Li, Haiqi

doi:10.3390/info11030166

Cited by 20 publications

(43 citation statements)

References 41 publications

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“…The well‐known “adult” table of the UCI machine learning repository is used in Experiment 1 50 . This table has been used in previous experiments 4,7,8,30‐37,39,40,45,51‐53 . Of the

15

attributes of this table, nine are used by Wang et al 30,31 For Experiment 1, these same nine are selected, namely age , workclass , education , marital‐status , occupation , race , sex , native‐country , and salary‐class , where salary‐class is the sensitive attribute.…”

Section: Resultsmentioning

confidence: 99%

“…Budiardjo, Wibowo, and Achsan present another model that distinguishes sensitive from nonsensitive values, albeit with the same demand that each equivalence class have at least

k

tuples, as well as in the special context of added sensitive attributes 34. Using

ℓ

‐diversity, Xiao and Li do something similar over attributes having values that are assigned one of three levels of sensitivity 35 . Unlike

p

‐proportion or (

α, k

)‐anonymity, Xiao and Li partition a table.…”

Section: Introductionmentioning

confidence: 99%

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Limiting sensitive values in an anonymized table while reducing information loss via p‐proportion

Dosselmann

Hamilton

2021

Security and Privacy

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The p‐proportion model bounds the proportion of sensitive values of a sensitive attribute in each equivalence class of an anonymized database table in order to limit the ability of a user to link an individual or entity to a sensitive value in that table. Nonsensitive values are not subject to any such constraints, which reduces the amount of anonymization needed to meet the requirements of this model. This leads to less information loss in an anonymized table. Anonymization is performed using an extension of the Mondrian algorithm that incorporates categorical attributes. Known as the adapted Mondrian algorithm, it generalizes a value of a categorical attribute to a set. Existing algorithms, by comparison, replace one value of a predefined hierarchy by another. The p‐proportion model is compared against the (α,k)‐anonymity model using both the progressive local recoding and (adapted) Mondrian algorithms. Experiments demonstrate the advantage of p‐proportion and Mondrian over (α,k)‐anonymity and progressive local recoding in terms of reduced information loss, measured using the normalized certainty penalty, discernibility metric, and classification metric.

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“…The well‐known “adult” table of the UCI machine learning repository is used in Experiment 1 50 . This table has been used in previous experiments 4,7,8,30‐37,39,40,45,51‐53 . Of the

15

Section: Resultsmentioning

confidence: 99%

“…Budiardjo, Wibowo, and Achsan present another model that distinguishes sensitive from nonsensitive values, albeit with the same demand that each equivalence class have at least

k

tuples, as well as in the special context of added sensitive attributes 34. Using

ℓ

‐diversity, Xiao and Li do something similar over attributes having values that are assigned one of three levels of sensitivity 35 . Unlike

p

‐proportion or (

α, k

)‐anonymity, Xiao and Li partition a table.…”

Section: Introductionmentioning

confidence: 99%

Limiting sensitive values in an anonymized table while reducing information loss via p‐proportion

Dosselmann

Hamilton

2021

Security and Privacy

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show abstract

“…Overlapped slicing method [63] 1 There is no much difference in execution time when compared with existing. Lsl-diversity mode [65]. 1:1 relational dataset, MSA.…”

Section: Pruning Strategies Suppression Techniquesmentioning

confidence: 99%

Heap Bucketization Anonymity—An Efficient Privacy-Preserving Data Publishing Model for Multiple Sensitive Attributes

et al. 2022

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The publication of a patient's dataset is essential for various medical investigations and decision-making. Currently, significant focus has been established to protect privacy during data publishing. The existing privacy models for multiple sensitive attributes do not concentrate on the correlation among the attributes, which in turn leads to much utility loss. An efficient model Heap Bucketization-anonymity (HBA) has been proposed to balance privacy and utility with multiple sensitive attributes. The Heap Bucketization-anonymity model used anatomization to vertically partition the dataset into 1. Quasi-identifier table and 2. Sensitive attribute table. The quasi-identifier is anonymized by implementing k-anonymity and slicing and the sensitive attributes are anonymized by applying slicing and Heap Bucketization. The metrics Normalized Certainty Penalty and KL-divergence have been used to compute the utility loss in the patient dataset. The experimental results show that the HB-anonymity can significantly achieve high privacy with less utility loss than other existing models. The HBanonymity model not only balances the utility and privacy also eradicates the i) background knowledge attack, ii) quasiidentifier attack iii) membership attack, iv) non-membership attack and v) fingerprint correlation attack.

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“…The innovative KCi-slice [ 34 ] is a KC-slice model enhancement with better privacy and utility requirements. The author of [ 35 ] proposed multiple security levels for different SAs values. The proposed method claims more utility, but requires more time to execute.…”

Section: Literature Reviewmentioning

confidence: 99%

F-Classify: Fuzzy Rule Based Classification Method for Privacy Preservation of Multiple Sensitive Attributes

Attaullah

Anjum

Kanwal

et al. 2021

Sensors

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With the advent of smart health, smart cities, and smart grids, the amount of data has grown swiftly. When the collected data is published for valuable information mining, privacy turns out to be a key matter due to the presence of sensitive information. Such sensitive information comprises either a single sensitive attribute (an individual has only one sensitive attribute) or multiple sensitive attributes (an individual can have multiple sensitive attributes). Anonymization of data sets with multiple sensitive attributes presents some unique problems due to the correlation among these attributes. Artificial intelligence techniques can help the data publishers in anonymizing such data. To the best of our knowledge, no fuzzy logic-based privacy model has been proposed until now for privacy preservation of multiple sensitive attributes. In this paper, we propose a novel privacy preserving model F-Classify that uses fuzzy logic for the classification of quasi-identifier and multiple sensitive attributes. Classes are defined based on defined rules, and every tuple is assigned to its class according to attribute value. The working of the F-Classify Algorithm is also verified using HLPN. A wide range of experiments on healthcare data sets acknowledged that F-Classify surpasses its counterparts in terms of privacy and utility. Being based on artificial intelligence, it has a lower execution time than other approaches.

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

Cited by 20 publications

References 41 publications

Limiting sensitive values in an anonymized table while reducing information loss via p‐proportion

Limiting sensitive values in an anonymized table while reducing information loss via p‐proportion

Heap Bucketization Anonymity—An Efficient Privacy-Preserving Data Publishing Model for Multiple Sensitive Attributes

F-Classify: Fuzzy Rule Based Classification Method for Privacy Preservation of Multiple Sensitive Attributes

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