Anonymizing Data with Relational and Transaction Attributes

Poulis, Giorgos; Loukides, Grigorios; Gkoulalas-Divanis, Aris; Skiadopoulos, Spiros

doi:10.1007/978-3-642-40994-3_23

Cited by 45 publications

(68 citation statements)

References 19 publications

(75 reference statements)

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“…Second, we describe (k, k m ) − anonymity property [21] that we impose on the research database and preserve while updating RSDB in the distributed environment. Finally, we present an overview of the related work and specify how they differ from our approach.…”

Section: Related Workmentioning

confidence: 99%

“…[11]) can be used for privacy preserving data publishing. However, Poulis et al show that all these methods are not appropriate for the anonymization of the datasets containing both relational (i.e., singlevalued) and transaction (i.e., set-valued) attributes, such as medical datasets that contain patient demographics and diagnosis information together [21].…”

Section: Anonymity Of Medical Datamentioning

confidence: 99%

“…(k, k m )−anonymity proposed in [21] ensures that for any record r in the dataset and any set of m or less items in transaction attribute of r, there should be at least (k − 1) records that are indistinguishable from record r. However, k − anonymity for relational attributes (i.e., existence of at least (k − 1) records that are indistinguishable from record r with respect to relational attributes of the record r) and (k m ) − anonymity for transaction attribute do not imply (k, k m ) − anonymity. Poulis et al developed two frameworks that produce (k, k m )−anonymous datasets with bounded information loss in one attribute type (relational or transaction) and minimal information loss in the other (transaction or relational).…”

Section: Anonymity Of Medical Datamentioning

confidence: 99%

“…We assume that RSDB is initialized as (k, k m ) − anonymous, i.e., an algorithm to achieve (k, k m ) − anonymity proposed in [21] had been applied to the local dataset to build the initial version of RSDB. For each qid there exist a binary tree, according to which generalization is performed.…”

Section: Anonymization Of Patients' Data For Research Purposesmentioning

confidence: 99%

See 3 more Smart Citations

A Cloud-Based eHealth Architecture for Privacy Preserving Data Integration

Dubovitskaya

Urovi

Vasirani

et al. 2015

ICT Systems Security and Privacy Protection

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Abstract. In this paper, we address the problem of building an anonymized medical database from multiple sources. Our proposed solution defines how to achieve data integration in a heterogeneous network of many clinical institutions, while preserving data utility and patients' privacy. The contribution of the paper is twofold: Firstly, we propose a secure and scalable cloud eHealth architecture to store and exchange patients' data for the treatment. Secondly, we present an algorithm for efficient aggregation of the health data for the research purposes from multiple sources independently.

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Section: Related Workmentioning

confidence: 99%

Section: Anonymity Of Medical Datamentioning

confidence: 99%

Section: Anonymity Of Medical Datamentioning

confidence: 99%

Section: Anonymization Of Patients' Data For Research Purposesmentioning

confidence: 99%

See 2 more Smart Citations

A Cloud-Based eHealth Architecture for Privacy Preserving Data Integration

Dubovitskaya

Urovi

Vasirani

et al. 2015

ICT Systems Security and Privacy Protection

View full text Add to dashboard Cite

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“…However, their authors recognize the need for algorithms that anonymize both demographics and diagnosis codes, in order to prevent identity disclosure [7] and increase data availability [36]. Also, publishing RT -datasets is important to support analysis tasks, including case count studies [46,54], which require accurately counting the number of patients associated with specific demographics and diagnosis codes, predictive modeling, and query answering [61].…”

Section: Introductionmentioning

confidence: 99%

Anonymizing datasets with demographics and diagnosis codes in the presence of utility constraints

Poulis

Loukides

Skiadopoulos

et al. 2017

Journal of Biomedical Informatics

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Publishing data about patients that contain both demographics and diagnosis codes is essential to perform large-scale, low-cost medical studies. However, preserving the privacy and utility of such data is challenging, because it requires: (i) guarding against identity disclosure (re-identification) attacks based on both demographics and diagnosis codes, (ii) ensuring that the anonymized data remain useful in intended analysis tasks, and (iii) minimizing the information loss, incurred by anonymization, to preserve the utility of general analysis tasks that are difficult to determine before data publishing. Existing anonymization approaches are not suitable for being used in this setting, because they cannot satisfy all three requirements. Therefore, in this work, we propose a new approach to deal with this problem. We enforce the requirement (i) by applying (k, k m )-anonymity, a privacy principle that prevents re-identification from attackers who know the demographics of a patient and up to m of their diagnosis codes, where k and m are tunable parameters. To capture the requirement (ii), we propose the concept of utility constraint for both demographics and diagnosis codes. Utility constraints limit the amount of generalization and are specified by data owners (e.g., the healthcare institution that performs anonymization). We also capture requirement (iii), by employing well-established information loss measures for demographics and for diagnosis codes. To realize our approach, we develop an algorithm that enforces (k, k m )-anonymity on a dataset containing both demographics and diagnosis codes, in a way that satisfies the specified utility constraints and with minimal information loss, according to the measures. Our experiments with a large dataset containing more than 200, 000 electronic health records show the effectiveness and efficiency of our algorithm.

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A new utility‐aware anonymization model for privacy preserving data publishing

Canbay

Sağıroğlu

Vural

2022

Concurrency and Computation

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Most of data in various forms contain sensitive information about individuals and so publishing such data might violate privacy. Privacy preserving data publishing (PPDP) is an essential for publishing useful data while preserving privacy. Anonymization, which is a utility based privacy preserving approach, helps hiding the identities of data subjects and also provides data utility. Since data utility is effective on the accuracy of analysis model, new anonymization algorithms to improve data utility are always required. Mondrian is one of the near-optimal anonymization models that presents high data utility and is frequently used for PPDP. However, the upper bound problem of Mondrian causes a decrease in potential data utility. This article focuses on this problem and proposes a new utility-aware anonymization model (u-Mondrian). Experimental results have shown that u-Mondrian presents an acceptable solution to the upper bound problem, increases total data utility and presents higher data utility than Mondrian for different partitioning strategies and datasets.

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Anonymizing Data with Relational and Transaction Attributes

Cited by 45 publications

References 19 publications

A Cloud-Based eHealth Architecture for Privacy Preserving Data Integration

A Cloud-Based eHealth Architecture for Privacy Preserving Data Integration

Anonymizing datasets with demographics and diagnosis codes in the presence of utility constraints

A new utility‐aware anonymization model for privacy preserving data publishing

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