A Sensitivity-Adaptive $$\rho $$-Uncertainty Model for Set-Valued Data

Chen, Liuhua; Su, Zhong; Wang, Lie; Li, Xianxian

doi:10.1007/978-3-662-54970-4_27

Cited by 4 publications

(6 citation statements)

References 22 publications

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“…Sun et al [32] extended the k-anonymity concept with two new properties (e.g., (p, α) and p + -sensitive k-anonymity) for controlling SA disclosure in PPDP. Similarly, Chen et al [33] proposed the ρ-uncertainty model to control over-suppression and generalization issues for privacy protection in set-valued data.…”

Section: Traditional Anonymization Methodsmentioning

confidence: 99%

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: Traditional Anonymization Methodsmentioning

confidence: 99%

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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“…In addition, there are some secure data publishing methods that are effective but not applicable to medical data, such as [44]. Because the sensitive data contained in medical data is not only the final label but also various sensitive attributes, the method in [44] requires a great limitation on the attackeraŕs background knowledge, and the security level is insufficient. However, not much is found in papers that address the privacy preservation to achieve the goal of classification [45], [54].…”

Section: Related Workmentioning

confidence: 99%

Differential Privacy for Data and Model Publishing of Medical Data

et al. 2019

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Combining medical data and machine learning has fully utilized the value of medical data. However, medical data contain a large amount of sensitive information, and the inappropriate handling of data can lead to the leakage of personal privacy. Thus, both publishing data and training data in machine learning may reveal the privacy of patients. To address the above issue, we propose two effective approaches. One combines a differential privacy and decision tree (DPDT) approach to provide strong privacy guarantees for publishing data, which establishes a weight calculation system based on the classification and regression tree (CART) method and takes weights as a new element of differential privacy to participate in privacy protection and reduce the negative impact of differential privacy on data availability. Another uses the differentially private mini-batch gradient descent algorithm (DPMB) to provide strong protection for training data; it tracks the privacy loss and allows the model to satisfy differential privacy in the process of gradient descent to prevent attackers from invading personal privacy with the training data. It is worth mentioning that, in this paper, we adopt the data processed by DPDT as the training data of DPMB to further strengthen the privacy of data. INDEX TERMS Deep learning, data privacy, differential privacy, data publishing.

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“…There are several works that have contributed to set-valued data anonymization [5,10,31]. However, those work used either generalization or suppression, and combination of both of them to obtain anonymized database.…”

Section: Related Workmentioning

confidence: 99%

“…ρ-uncertainty proposed in [11] utilizes partial suppression based on heuristic approach which removes only items in sensitive association rules to avoid excessive information loss. By improving the previous work, a scheme in [5] combining local generalization and partial suppression considers the sensitivity degree of each item as the parameter value to generate anonymized database. As a trajectory data anonymization, (K, C) L -privacy is proposed in [6] based on local suppression method which removes sequential trajectory that violates the privacy based on the given K, C and L values.…”

Section: Related Workmentioning

confidence: 99%

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Set-valued Data Anonymization Maintaining Data Utility and Data Property

Gunawan

Mambo

2018

Proceedings of the 12th International Conference on Ubiquitous Information Management and Communication

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Set-valued database publication has been attracting much attention due to its benefit for various applications like recommendation systems and marketing analysis. However, publishing original database directly is risky since an unauthorized party may violate individual privacy by associating and analyzing relations between individuals and set of items in the published database, which is known as identity linkage attack. Generally, an attack is performed based on attacker's background knowledge obtained by a prior investigation and such adversary knowledge should be taken into account in the data anonymization. Various data anonymization schemes have been proposed to prevent the identity linkage attack. However, in existing data anonymization schemes, either data utility or data property is reduced a lot after excessive database modification and consequently data recipients become to distrust the released database. In this paper, we propose a new data anonymization scheme, called sibling suppression, which causes minimum data utility lost and maintains data properties like database size and the number of records. The scheme uses multiple sets of adversary knowledge and items in a category of adversary knowledge are replaced by other items in the category. Several experiments with real dataset show that our method can preserve data utility with minimum lost and maintain data property as the same as original database. CCS CONCEPTS • Security and privacy → Data anonymization and sanitization; Database and storage security;

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A Sensitivity-Adaptive $$\rho $$-Uncertainty Model for Set-Valued Data

Cited by 4 publications

References 22 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

Differential Privacy for Data and Model Publishing of Medical Data

Set-valued Data Anonymization Maintaining Data Utility and Data Property

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