G-Model: A Novel Approach to Privacy-Preserving 1:M Microdata Publication

Albulayhi, Khalid; Tošić, Predrag T.; Sheldon, Frederick T.

doi:10.1109/cscloud-edgecom49738.2020.00024

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…This approach deals with achieving utility, but that may not provide enough security. The generic 1:M data privacy (G-model) model uses the signature method to achieve privacy ( Albulayhi, Tošić & Sheldon, 2020 ). However, it has a limitation in which attackers can attack using the signature values to spot records.…”

Section: Related Workmentioning

confidence: 99%

Improved angelization technique against background knowledge attack for 1:M microdata

Fazal

Khan

Anjum

et al. 2023

PeerJ Computer Science

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With the advent of modern information systems, sharing Electronic Health Records (EHRs) with different organizations for better medical treatment, and analysis is beneficial for both academic as well as for business development. However, an individual’s personal privacy is a big concern because of the trust issue across organizations. At the same time, the utility of the shared data that is required for its favorable use is also important. Studies show that plenty of conventional work is available where an individual has only one record in a dataset (1:1 dataset), which is not the case in many applications. In a more realistic form, an individual may have more than one record in a dataset (1:M). In this article, we highlight the high utility loss and inapplicability for the 1:M dataset of the θ-Sensitive k-Anonymity privacy model. The high utility loss and low data privacy of (p, l)-angelization, and (k, l)-diversity for the 1:M dataset. As a mitigation solution, we propose an improved (θ∗, k)-utility algorithm to preserve enhanced privacy and utility of the anonymized 1:M dataset. Experiments on the real-world dataset reveal that the proposed approach outperforms its counterpart, in terms of utility and privacy for the 1:M dataset.

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Section: Related Workmentioning

confidence: 99%

Improved angelization technique against background knowledge attack for 1:M microdata

Fazal

Khan

Anjum

et al. 2023

PeerJ Computer Science

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“…Gmodel provides a proper balance between utility and privacy. It protects the 1:M microdata from gender precise sensitive attribute attacks [42]. An f-slip model has been proposed for 1:M microdata.…”

Section: :M Micro Datamentioning

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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A Comparative Study for Anonymizing Datasets with Multiple Sensitive Attributes and Multiple Records

Nasr

Sayed

Ead

2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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G-Model: A Novel Approach to Privacy-Preserving 1:M Microdata Publication

Cited by 3 publications

References 23 publications

Improved angelization technique against background knowledge attack for 1:M microdata

Improved angelization technique against background knowledge attack for 1:M microdata

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

A Comparative Study for Anonymizing Datasets with Multiple Sensitive Attributes and Multiple Records

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