Data anonymization evaluation against re-identification attacks in edge storage

Chen, Ming‐Hui; Cang, Li Shan; Chang, Zheng; Iqbal, Muddesar; Almakhles, Dhafer

doi:10.1007/s11276-023-03235-6

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…The proposed algorithm safeguards privacy breaches against background knowledge attacks while sustaining data utility and privacy. Chen et al [50] devised a framework to measure the level of privacy and usefulness offered by the anonymized datasets. Xia et al [51] developed a clustering and DP-based model for reducing IL in data-releasing scenarios.…”

Section: Anonymization Methods For Enhancing Data Utilitymentioning

confidence: 99%

“…All three metrics (DM, accuracy, and PD) were used to measure the effectiveness of the proposed approach. For fair comparisons, we prepared anonymized versions of both datasets with varying scales of k. We used both large-scale values (L s , where L s = D k and k = [50,75,100,125,150,175,200,250]), and small-scale values (S s , where S s = D k and k = [5,10,15,20,25,30,35,40]) of k to demonstrate the potency of the proposed approach.…”

Section: Accuracy =mentioning

confidence: 99%

“…For example, in the Adult dataset, there are only two values of the SA, and the distribution is highly skewed. To further highlight the trade-off between the privacy/risk and utility of the proposed approach, we employed the re-identification risk against homogeneous attacks [50]. The HA value can be computed using Equation (21).…”

Section: Comparison Of Individual Privacy Preservationmentioning

confidence: 99%

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A Generic Approach towards Enhancing Utility and Privacy in Person-Specific Data Publishing Based on Attribute Usefulness and Uncertainty

Majeed

Hwang

2023

Electronics

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This paper proposes a generic anonymization approach for person-specific data, which retains more information for data mining and analytical purposes while providing considerable privacy. The proposed approach takes into account the usefulness and uncertainty of attributes while anonymizing the data to significantly enhance data utility. We devised a method for determining the usefulness weight for each attribute item in a dataset, rather than manually deciding (or assuming based on domain knowledge) that a certain attribute might be more useful than another. We employed an information theory concept for measuring the uncertainty regarding sensitive attribute’s value in equivalence classes to prevent unnecessary generalization of data. A flexible generalization scheme that simultaneously considers both attribute usefulness and uncertainty is suggested to anonymize person-specific data. The proposed methodology involves six steps: primitive analysis of the dataset, such as analyzing attribute availability in the data, arranging the attributes into relevant categories, and sophisticated pre-processing, computing usefulness weights of attributes, ranking users based on similarities, computing uncertainty in sensitive attributes (SAs), and flexible data generalization. Our methodology offers the advantage of retaining higher truthfulness in data without losing guarantees of privacy. Experimental analysis on two real-life benchmark datasets with varying scales, and comparisons with prior state-of-the-art methods, demonstrate the potency of our anonymization approach. Specifically, our approach yielded better performance on three metrics, namely accuracy, information loss, and disclosure risk. The accuracy and information loss were improved by restraining heavier anonymization of data, and disclosure risk was improved by preserving higher uncertainty in the SA column. Lastly, our approach is generic and can be applied to any real-world person-specific tabular datasets encompassing both demographics and SAs of individuals.

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Section: Anonymization Methods For Enhancing Data Utilitymentioning

confidence: 99%

Section: Accuracy =mentioning

confidence: 99%

Section: Comparison Of Individual Privacy Preservationmentioning

confidence: 99%

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A Generic Approach towards Enhancing Utility and Privacy in Person-Specific Data Publishing Based on Attribute Usefulness and Uncertainty

Majeed

Hwang

2023

Electronics

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“…More recent work has explored the use of machine learning techniques for de-anonymization [12,13]. Rocher et al [14] proposed an approach to estimate the risk of re-identification in anonymized datasets using machine learning models trained on auxiliary data.…”

Section: De-anonymization Techniquesmentioning

confidence: 99%

De-Anonymizing Users across Rating Datasets via Record Linkage and Quasi-Identifier Attacks

Torres,

Olivares

2024

Data

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The widespread availability of pseudonymized user datasets has enabled personalized recommendation systems. However, recent studies have shown that users can be de-anonymized by exploiting the uniqueness of their data patterns, raising significant privacy concerns. This paper presents a novel approach that tackles the challenging task of linking user identities across multiple rating datasets from diverse domains, such as movies, books, and music, by leveraging the consistency of users’ rating patterns as high-dimensional quasi-identifiers. The proposed method combines probabilistic record linkage techniques with quasi-identifier attacks, employing the Fellegi–Sunter model to compute the likelihood of two records referring to the same user based on the similarity of their rating vectors. Through extensive experiments on three publicly available rating datasets, we demonstrate the effectiveness of the proposed approach in achieving high precision and recall in cross-dataset de-anonymization tasks, outperforming existing techniques, with F1-scores ranging from 0.72 to 0.79 for pairwise de-anonymization tasks. The novelty of this research lies in the unique integration of record linkage techniques with quasi-identifier attacks, enabling the effective exploitation of the uniqueness of rating patterns as high-dimensional quasi-identifiers to link user identities across diverse datasets, addressing a limitation of existing methodologies. We thoroughly investigate the impact of various factors, including similarity metrics, dataset combinations, data sparsity, and user demographics, on the de-anonymization performance. This work highlights the potential privacy risks associated with the release of anonymized user data across diverse contexts and underscores the critical need for stronger anonymization techniques and tailored privacy-preserving mechanisms for rating datasets and recommender systems.

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A Privacy Preserving Service-Oriented Approach for Data Anonymization Through Deep Learning

Giampaolo,

Izzo,

Prezioso

et al. 2023

2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf

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Data anonymization evaluation against re-identification attacks in edge storage

Cited by 6 publications

References 28 publications

A Generic Approach towards Enhancing Utility and Privacy in Person-Specific Data Publishing Based on Attribute Usefulness and Uncertainty

A Generic Approach towards Enhancing Utility and Privacy in Person-Specific Data Publishing Based on Attribute Usefulness and Uncertainty

De-Anonymizing Users across Rating Datasets via Record Linkage and Quasi-Identifier Attacks

A Privacy Preserving Service-Oriented Approach for Data Anonymization Through Deep Learning

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