Sensitive Label Privacy Preservation with Anatomization for Data Publishing

Yao, Lin; Chen, Zhenyu; Wang, Xin; Liu, Dong; Wu, Guowei

doi:10.1109/tdsc.2019.2919833

Cited by 20 publications

(12 citation statements)

References 42 publications

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“…In the table division procedure, the meansquare contingency coefficient and entropy have been adopted for anonymization. In group division, nonoverlapping groups have been framed to satisfy the (α,β,γ,δ) model [29].…”

Section: A 1:1 Micro Data With Single Sensitive Attributementioning

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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Section: A 1:1 Micro Data With Single Sensitive Attributementioning

confidence: 99%

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

et al. 2022

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“…Anatomisation . The goal of Anatomisation is to de‐link the association among attributes without modifying their values [19]. Xiao et al.…”

Section: Related Workmentioning

confidence: 99%

Medical data publishing based on average distribution and clustering

Shi

Zhu

2022

CAAI Trans on Intel Tech

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Most of the data publishing methods have not considered sensitivity protection, and hence the adversary can disclose privacy by sensitivity attack. Faced with this problem, this paper presents a medical data publishing method based on sensitivity determination. To protect the sensitivity, the sensitivity of disease information is determined by semantics. To seek the trade‐off between information utility and privacy security, the new method focusses on the protection of sensitive values with high sensitivity and assigns the highly sensitive disease information to groups as evenly as possible. The experiments are conducted on two real‐world datasets, of which the records include various attributes of patients. To measure sensitivity protection, the authors define a metric, which can evaluate the degree of sensitivity disclosure. Besides, additional information loss and discernability metrics are used to measure the availability of released tables. The experimental results indicate that the new method can provide better privacy than the traditional one while the information utility is guaranteed. Besides value protection, the proposed method can provide sensitivity protection and available releasing for medical data.

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“…As a primary anonymization technique for privacy-preserving data publishing, bucketization partitions datasets into non-overlapped subsets to de-link the relation between attributes without modifying published data [32]. Anatomy [33] is the first proposed bucketization technique to protect sensitive information in relational datasets, where quasiidentifiers and sensitive values are first separated into two tables, and each table is divided into buckets.…”

Section: On Bucketization Techniquementioning

confidence: 99%

“…Based on anatomy, Liu et al [16] design a linear time algorithm for the l-diverse dataset in the published social graph. A relational data anonymization scheme using anatomization, called SLPPA [32], performs the table and group divisions to achieve the (α, β, γ, δ)-privacy requirement. Slicing [36] is another bucketization-based approach that first vertically partitions attributes into columns and then horizontally divides tuples into buckets to meet l-diversity.…”

Section: On Bucketization Techniquementioning

confidence: 99%

“…These algorithms can be categorized according to the attack form, privacy principles, and data transformation strategy, as shown in Table 1. Techniques based on generalization or perturbation often modify the attribute values, reducing data utility for analysis [32]. Anatomy [33] and slicing [36] are often used to anonymize relational data with a fixed number of attributes in anatomization-based techniques, while disassociation is tailored for transactional data anonymization.…”

Section: Summary Of Related Workmentioning

confidence: 99%

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Transactional Data Anonymization for Privacy and Information Preservation via Disassociation and Local Suppression

2022

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Ubiquitous devices in IoT-based environments create a large amount of transactional data on daily personal behaviors. Releasing these data across various platforms and applications for data mining can create tremendous opportunities for knowledge-based decision making. However, solid guarantees on the risk of re-identification are required to make these data broadly available. Disassociation is a popular method for transactional data anonymization against re-identification attacks in privacy-preserving data publishing. The anonymization algorithm of disassociation is performed in parallel, suitable for the asymmetric paralleled data process in IoT where the nodes have limited computation power and storage space. However, the anonymization algorithm of disassociation is based on the global recoding mode to achieve transactional data km-anonymization, which leads to a loss of combinations of items in transactional datasets, thus decreasing the data quality of the published transactions. To address the issue, we propose a novel vertical partition strategy in this paper. By employing local suppression and global partition, we first eliminate the itemsets which violate km-anonymity to construct the first km-anonymous record chunk. Then, by the processes of itemset creating and reducing, we recombine the globally partitioned items from the first record chunk to construct remaining km-anonymous record chunks. The experiments illustrate that our scheme can retain more association between items in the dataset, which improves the utility of published data.

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Sensitive Label Privacy Preservation with Anatomization for Data Publishing

Cited by 20 publications

References 42 publications

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

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

Medical data publishing based on average distribution and clustering

Transactional Data Anonymization for Privacy and Information Preservation via Disassociation and Local Suppression

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