Privacy preserving data publishing of categorical data through
            <i>k</i>
            ‐anonymity and feature selection

Aristodimou, Aristos; Αντωνιάδης, Άθως; Pattichis, Constantinos S.

doi:10.1049/htl.2015.0050

Cited by 19 publications

(14 citation statements)

References 20 publications

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“…This thereby allows the use of the results for research purposes. The data are anonymized—including the geolocalized data—using k‐anonymity . An Independent Review Board approval was not needed for this observational study.…”

Section: Methodsmentioning

confidence: 99%

Adherence to treatment in allergic rhinitis using mobile technology. The MASK Study

Menditto¹,

Costa²,

Midão³

et al. 2019

Clin Experimental Allergy

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Background: Mobile technology may help to better understand the adherence to treatment. MASK-rhinitis (Mobile Airways Sentinel NetworK for allergic rhinitis) is a patient-centred ICT system. A mobile phone app (the Allergy Diary) central to MASK is available in 22 countries. Objectives:To assess the adherence to treatment in allergic rhinitis patients using the Allergy Diary App. Methods:An observational cross-sectional study was carried out on all users who filled in the Allergy Diary from 1 January 2016 to 1 August 2017. Secondary adherence was assessed by using the modified Medication Possession Ratio (MPR) and the Proportion of days covered (PDC) approach.Results: A total of 12 143 users were registered. A total of 6 949 users reported at least one VAS data recording. Among them, 1 887 users reported ≥7 VAS data.About 1 195 subjects were included in the analysis of adherence. One hundred and thirty-six (11.28%) users were adherent (MPR ≥70% and PDC ≤1.25), 51 (4.23%) were partly adherent (MPR ≥70% and PDC = 1.50) and 176 (14.60%) were switchers. On the other hand, 832 (69.05%) users were non-adherent to medications (MPR <70%). Of those, the largest group was non-adherent to medications and the time interval was increased in 442 (36.68%) users. Conclusion and clinical relevance:Adherence to treatment is low. The relative efficacy of continuous vs on-demand treatment for allergic rhinitis symptoms is still a matter of debate. This study shows an approach for measuring retrospective adherence based on a mobile app. This also represents a novel approach for analysing medication-taking behaviour in a real-world setting. K E Y W O R D S adherence, mHealth, mobile technology, observational study, rhinitis, treatment S U P P O R T I N G I N F O R M A T I O N Additional supporting information may be found online in the Supporting Information section at the end of the article. How to cite this article: Menditto E, Costa E, Midão L, et al. the MASK group. Adherence to treatment in allergic rhinitis using mobile technology. The MASK Study. Clin Exp Allergy.

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Section: Methodsmentioning

confidence: 99%

Adherence to treatment in allergic rhinitis using mobile technology. The MASK Study

Menditto¹,

Costa²,

Midão³

et al. 2019

Clin Experimental Allergy

View full text Add to dashboard Cite

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“…The Allergy Diary is completely anonymized except for the geolocation aspect that has been pseudo-anonymized. We have now used k-anonymity [ 64 ] to fully anonymize geolocation [ 65 ]. We are updating the ethical approval for POLLAR in order to comply with the GDPR [ 66 ].…”

Section: Pollarmentioning

confidence: 99%

POLLAR: Impact of air POLLution on Asthma and Rhinitis; a European Institute of Innovation and Technology Health (EIT Health) project

Bousquet

Antó

Annesi‐Maesano

et al. 2018

Clin Transl Allergy

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Allergic rhinitis (AR) is impacted by allergens and air pollution but interactions between air pollution, sleep and allergic diseases are insufficiently understood. POLLAR (Impact of air POLLution on sleep, Asthma and Rhinitis) is a project of the European Institute of Innovation and Technology (EIT Health). It will use a freely-existing application for AR monitoring that has been tested in 23 countries (the Allergy Diary, iOS and Android, 17,000 users, TLR8). The Allergy Diary will be combined with a new tool allowing queries on allergen, pollen (TLR2), sleep quality and disorders (TRL2) as well as existing longitudinal and geolocalized pollution data. Machine learning will be used to assess the relationship between air pollution, sleep and AR comparing polluted and non-polluted areas in 6 EU countries. Data generated in 2018 will be confirmed in 2019 and extended by the individual prospective assessment of pollution (portable sensor, TLR7) in AR. Sleep apnea patients will be used as a demonstrator of sleep disorder that can be modulated in terms of symptoms and severity by air pollution and AR. The geographic information system GIS will map the results. Consequences on quality of life (EQ-5D), asthma, school, work and sleep will be monitored and disseminated towards the population. The impacts of POLLAR will be (1) to propose novel care pathways integrating pollution, sleep and patients’ literacy, (2) to study sleep consequences of pollution and its impact on frequent chronic diseases, (3) to improve work productivity, (4) to propose the basis for a sentinel network at the EU level for pollution and allergy, (5) to assess the societal implications of the interaction. MASK paper N°32.

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“…Several studies were conducted to anonymize data based on these definitions using suppression and generalization operations. For example, Aristodimou et al (45) proposed a pattern-based multidimensional suppression technique (kPB-MS) for privacy-preserving data publishing to minimize the information loss through k -anonymity in which feature selection was incorporated to reduce data dimensionality and combine attributes for record suppression. Yoo et al (46) described a generalization method satisfying both k -anonymity and l -diversity that used conditional entropy to measure the loss of information as well as mutual information for sensitive attributes.…”

Section: Technologymentioning

confidence: 99%

Privacy Policy and Technology in Biomedical Data Science

Arellano

Dai

Wang

et al. 2018

Annu. Rev. Biomed. Data Sci.

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Privacyis an important consideration when sharing clinical data, which often contain sensitive information. Adequate protection to safeguard patient privacy and to increase public trust in biomedical research is paramount. This review covers topics in policy and technology in the context of clinical data sharing. We review policy articles related to (a) the Common Rule, HIPAA privacy and security rules, and governance; (b) patients’ viewpoints and consent practices; and (c) research ethics. We identify key features of the revised Common Rule and the most notable changes since its previous version. We address data governance for research in addition to the increasing emphasis on ethical and social implications. Research ethics topics include data sharing best practices, use of data from populations of low socioeconomic status (SES), recent updates to institutional review board (IRB) processes to protect human subjects’ data, and important concerns about the limitations of current policies to address data deidentification. In terms of technology, we focus on articles that have applicability in real world health care applications: deidentification methods that comply with HIPAA, data anonymization approaches to satisfy well-acknowledged issues in deidentified data, encryption methods to safeguard data analyses, and privacy-preserving predictive modeling. The first two technology topics are mostly relevant to methodologies that attempt to sanitize structured or unstructured data. The third topic includes analysis on encrypted data. The last topic includes various mechanisms to build statistical models without sharing raw data.

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Privacy preserving data publishing of categorical data through k ‐anonymity and feature selection

Cited by 19 publications

References 20 publications

Adherence to treatment in allergic rhinitis using mobile technology. The MASK Study

Adherence to treatment in allergic rhinitis using mobile technology. The MASK Study

POLLAR: Impact of air POLLution on Asthma and Rhinitis; a European Institute of Innovation and Technology Health (EIT Health) project

Privacy Policy and Technology in Biomedical Data Science

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