PrioPrivacy: A Local Recoding K-Anonymity Tool for Prioritised Quasi-Identifiers

Bampoulidis, Alexandros; Markopoulos, Ioannis; Lupu, Mihai

doi:10.1145/3358695.3360918

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…PrioPrivacy has been developed by the Research Studio Data Science in Vienna, Austria, and was originally published in 2019 [ 54 ]. It is a tool for anonymizing tabular data, which intends to provide high flexibility in the choice of variables that need to be protected and the specification of their relevance for the utility of the resulting anonymized data.…”

Section: Resultsmentioning

confidence: 99%

Open tools for quantitative anonymization of tabular phenotype data: literature review

Haber

Sax

Praßer

2022

Briefings in Bioinformatics

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Precision medicine relies on molecular and systems biology methods as well as bidirectional association studies of phenotypes and (high-throughput) genomic data. However, the integrated use of such data often faces obstacles, especially in regards to data protection. An important prerequisite for research data processing is usually informed consent. But collecting consent is not always feasible, in particular when data are to be analyzed retrospectively. For phenotype data, anonymization, i.e. the altering of data in such a way that individuals cannot be identified, can provide an alternative. Several re-identification attacks have shown that this is a complex task and that simply removing directly identifying attributes such as names is usually not enough. More formal approaches are needed that use mathematical models to quantify risks and guide their reduction. Due to the complexity of these techniques, it is challenging and not advisable to implement them from scratch. Open software libraries and tools can provide a robust alternative. However, also the range of available anonymization tools is heterogeneous and obtaining an overview of their strengths and weaknesses is difficult due to the complexity of the problem space. We therefore performed a systematic review of open anonymization tools for structured phenotype data described in the literature between 1990 and 2021. Through a two-step eligibility assessment process, we selected 13 tools for an in-depth analysis. By comparing the supported anonymization techniques and further aspects, such as maturity, we derive recommendations for tools to use for anonymizing phenotype datasets with different properties.

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Section: Resultsmentioning

confidence: 99%

Open tools for quantitative anonymization of tabular phenotype data: literature review

Haber

Sax

Praßer

2022

Briefings in Bioinformatics

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“…We are going to revisit this procedure in the context of the H2020 TRUSTS project, apply it on its use-case datasets, and use PrioPrivacy [1], the anonymisation tool we developed, inspired by applying this procedure on FNET's data, and which is capable of outperforming ARX.…”

Section: Discussionmentioning

confidence: 99%

Practice and Challenges of (De-)Anonymisation for Data Sharing

Bampoulidis

Bruni

Markopoulos

et al. 2020

Research Challenges in Information Science

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Personal data is a necessity in many fields for research and innovation purposes, and when such data is shared, the data controller carries the responsibility of protecting the privacy of the individuals contained in their dataset. The removal of direct identifiers, such as full name and address, is not enough to secure the privacy of individuals as shown by de-anonymisation methods in the scientific literature. Data controllers need to become aware of the risks of de-anonymisation and apply the appropriate anonymisation measures before sharing their datasets, in order to comply with privacy regulations. To address this need, we defined a procedure that makes data controllers aware of the de-anonymisation risks and helps them in deciding the anonymisation measures that need to be taken in order to comply with the General Data Protection Regulation (GDPR). We showcase this procedure with a customer relationship management (CRM) dataset provided by a telecommunications provider. Finally, we recount the challenges we identified during the definition of this procedure and by putting existing knowledge and tools into practice.

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“…Wireless Communications and Mobile Computing records, lacking a public method that could be used for general data publishing. Bampoulidis and others [7] assume that some QIDs are more important than others (i.e., in data mining/analysis) and, therefore, should be distorted as little as possible in the anonymization process. They present a tool to address the issue of QIDs by utilizing a local recoding algorithm for k-anonymity.…”

Section: Related Workmentioning

confidence: 99%

“…The dataset attributes are divided into three divisions which are (1) data of bank clients: age, job, marital status, education, default, balance, housing, and loan; in this paper, we will consider these attributes because these attributes are significant for bank clients and reidentification purposes; (2) data related to the last contact of the current campaign; and (3) other attributes like the campaign and days. The second dataset is the adult dataset [53] used as a standard for anonymization algorithm evaluation [7] consisting of 48,842 census records and 15 attributes. ARX data anonymization software is open source introduced and developed by Prasser et al [54] for data anonymization; we used it to implement the algorithms as explained in the following sections.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…Serious issues that lead to identity disclosure are quasi-identifier (QID) value linking and the attacker's knowledge background. The QIDs are the dataset attributes that if each of them is considered separately does not distinguish the individual, but when several attributes are combined they can give a distinctive identification of individuals [7]. For example, when looking at the attributes of date of birth, gender, and ZIP code together, one can reidentify the individuals as stated in [8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quasi‐Identifier Recognition Algorithm for Privacy Preservation of Cloud Data Based on Risk Reidentification

Mansour

Siraj

Ghaleb

et al. 2021

Wireless Communications and Mobile Computing

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Cloud computing plays an essential role as a source for outsourcing data to perform mining operations or other data processing, especially for data owners who do not have sufficient resources or experience to execute data mining techniques. However, the privacy of outsourced data is a serious concern. Most data owners are using anonymization-based techniques to prevent identity and attribute disclosures to avoid privacy leakage before outsourced data for mining over the cloud. In addition, data collection and dissemination in a resource-limited network such as sensor cloud require efficient methods to reduce privacy leakage. The main issue that caused identity disclosure is quasi-identifier (QID) linking. But most researchers of anonymization methods ignore the identification of proper QIDs. This reduces the validity of the used anonymization methods and may thus lead to a failure of the anonymity process. This paper introduces a new quasi-identifier recognition algorithm that reduces identity disclosure which resulted from QID linking. The proposed algorithm is comprised of two main stages: (1) attribute classification (or QID recognition) and (2) QID dimension identification. The algorithm works based on the reidentification of risk rate for all attributes and the dimension of QIDs where it determines the proper QIDs and their suitable dimensions. The proposed algorithm was tested on a real dataset. The results demonstrated that the proposed algorithm significantly reduces privacy leakage and maintains the data utility compared to recent related algorithms.

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PrioPrivacy: A Local Recoding K-Anonymity Tool for Prioritised Quasi-Identifiers

Cited by 7 publications

References 8 publications

Open tools for quantitative anonymization of tabular phenotype data: literature review

Open tools for quantitative anonymization of tabular phenotype data: literature review

Practice and Challenges of (De-)Anonymisation for Data Sharing

Quasi‐Identifier Recognition Algorithm for Privacy Preservation of Cloud Data Based on Risk Reidentification

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