An Extensive Study on Data Anonymization Algorithms Based on K-Anonymity

Simi, M S; Nayaki, K. Sankara; Elayidom, M. Sudheep

doi:10.1088/1757-899x/225/1/012279

Cited by 13 publications

(5 citation statements)

References 8 publications

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“…Pureness is therefore more suitable than the classiication metric to measure how often the anonymous counterfactual explanation gives us correct advice. We choose the datasets described in Table 5, as they are all tabular datasets that contain various personal attributes through which individuals could be identiied, and are often used in research about privacy-preserving data mining [26,50,51]. 13 All these datasets contain private information such as inancial and health data that people generally do not want to be made public.…”

Section: = ( )mentioning

confidence: 99%

The Privacy Issue of Counterfactual Explanations: Explanation Linkage Attacks

Goethals

Sörensen

Martens

2023

ACM Trans. Intell. Syst. Technol.

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Black-box machine learning models are used in an increasing number of high-stakes domains, and this creates a growing need for Explainable AI (XAI). However, the use of XAI in machine learning introduces privacy risks, which currently remain largely unnoticed. Therefore, we explore the possibility of an explanation linkage attack , which can occur when deploying instance-based strategies to find counterfactual explanations. To counter such an attack, we propose k -anonymous counterfactual explanations and introduce pureness as a metric to evaluate the validity of these k -anonymous counterfactual explanations. Our results show that making the explanations, rather than the whole dataset, k -anonymous, is beneficial for the quality of the explanations.

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Section: = ( )mentioning

confidence: 99%

The Privacy Issue of Counterfactual Explanations: Explanation Linkage Attacks

Goethals

Sörensen

Martens

2023

ACM Trans. Intell. Syst. Technol.

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“…At the operational level, anonymisation and pseudonymisation call for specific techniques. For anonymisation, common practices include generalisation, k-anonymity and randomisation, to name just a few (European Commission, 2014; Simi et al, 2017; Sweeney, 2002). For pseudonymisation, the most common techniques employed are counter, random number generator, cryptographic hash function, message authentication code and encryption (European Union, 2019).…”

Section: Data Management Anonymisation and Pseudonymisationmentioning

confidence: 99%

Towards Open Science for the Qualitative Researcher: From a Positivist to an Open Interpretation

Class

Bruyne

Wuillemin

et al. 2021

International Journal of Qualitative Methods

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This reflection by a qualitative researcher stems from a concrete experience with data handling in a funded research project. The researcher followed Open Research Data guidelines and found optimal solutions to pseudonymise data, but this later evolved into a deep epistemological questioning on praxis. During the first phase of the project, a tailor-made software was developed with help from librarians and an IT professional to automate the pseudonymisation of the 150 data chunks generated by 16 students, 3 tutors and 3 decision makers. In the second phase of the project, this experience sparked questions about the meaning of such data handling and interpretations of Open Science, which led the researcher to suggest a framework for the professional development of qualitative researchers in their understanding of Open Science. The article raises awareness of normative frameworks in institutional data handling practices and calls for active contributions to defining qualitative research in an Open Science perspective, particularly taking as a reference the recent draft recommendation by UNESCO (2020)

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“…The research of privacy-preserving outsourced data focuses on anonymization-based methods [12][13][14][15][16][17][18], cryptographicbased methods [19][20][21][22][23][24], hybrid methods [2,[25][26][27], and methods that seek to improve the data utility [26,28,29]. Some recent studies have demonstrated the privacy requirements of incremental datasets [30][31][32] and multiple sensitive attributes [33][34][35].…”

Section: Related Workmentioning

confidence: 99%

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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An Extensive Study on Data Anonymization Algorithms Based on K-Anonymity

Cited by 13 publications

References 8 publications

The Privacy Issue of Counterfactual Explanations: Explanation Linkage Attacks

The Privacy Issue of Counterfactual Explanations: Explanation Linkage Attacks

Towards Open Science for the Qualitative Researcher: From a Positivist to an Open Interpretation

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

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