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The Costs of Anonymization: Case Study Using Clinical Data

The Costs of Anonymization: Case Study Using Clinical Data

Privacy-enhancing technologies, including anonymization algorithms, can maintain the privacy of study participants when sharing data [10,11]. Anonymization reduces privacy risks by altering data in a manner such that it is highly unlikely that it can be related to a person. Anonymization can be performed using various transformation mechanisms, such as suppression, randomization, or generalization.

Lisa Pilgram, Thierry Meurers, Bradley Malin, Elke Schaeffner, Kai-Uwe Eckardt, Fabian Prasser, GCKD Investigators

J Med Internet Res 2024;26:e49445

Privacy of Study Participants in Open-access Health and Demographic Surveillance System Data: Requirements Analysis for Data Anonymization

Privacy of Study Participants in Open-access Health and Demographic Surveillance System Data: Requirements Analysis for Data Anonymization

Note that for some selected data sets and general anonymization problems, the World Bank Group, PARIS21 and Organization for Economic Cooperation and Development, and the International Household Survey Network supported the development of the anonymization software sdc Micro [7], and they all recommend it [8]. sdc Micro is actively used in many organizations, ranging from statistical offices [9] and social and political science [10] to the United Nations High Commissioner for Refugees [11] and health [12-14].

Matthias Templ, Chifundo Kanjala, Inken Siems

JMIR Public Health Surveill 2022;8(9):e34472

“A Question of Trust” and “a Leap of Faith”—Study Participants’ Perspectives on Consent, Privacy, and Trust in Smart Home Research: Qualitative Study

“A Question of Trust” and “a Leap of Faith”—Study Participants’ Perspectives on Consent, Privacy, and Trust in Smart Home Research: Qualitative Study

The focus of this paper is to describe the SPHERE-CARED (Consent and Anonymization: A Review of Ethical Dimensions) study, exploring SPHERE participants’ perspectives on the ethical aspects of informed consent, anonymity, privacy, and data sharing. For a list of the SPHERE sensor technologies, including wearables, ambient sensors, and cameras, the interested reader should refer to the literature [8,17].

Mari-Rose Kennedy, Richard Huxtable, Giles Birchley, Jonathan Ives, Ian Craddock

JMIR Mhealth Uhealth 2021;9(11):e25227

Data Anonymization for Pervasive Health Care: Systematic Literature Mapping Study

Data Anonymization for Pervasive Health Care: Systematic Literature Mapping Study

This fact combined with the need for more accurate models in health care provides sufficient motivation for continued research into methods of data anonymization. For this study, we believe that how anonymization is defined is problem dependent. We reiterate that there is no clear-cut line between pseudonymization and anonymization because even anonymized data can practically have different reidentification risks [78,79] (depending on the type of anonymization performed).

Zheming Zuo, Matthew Watson, David Budgen, Robert Hall, Chris Kennelly, Noura Al Moubayed

JMIR Med Inform 2021;9(10):e29871

Revolutionizing Medical Data Sharing Using Advanced Privacy-Enhancing Technologies: Technical, Legal, and Ethical Synthesis

Revolutionizing Medical Data Sharing Using Advanced Privacy-Enhancing Technologies: Technical, Legal, and Ethical Synthesis

However, given the increased sophistication of reidentification attacks [10-16] and the rising dimensionality (number of clinical and genetic attributes) of patient data, the above-mentioned countermeasures are inadequate to ensure a proper level of anonymization and preserve acceptable data utility. As a result, these conventional anonymization techniques for individual-level patient data are rarely used in practice.

James Scheibner, Jean Louis Raisaro, Juan Ramón Troncoso-Pastoriza, Marcello Ienca, Jacques Fellay, Effy Vayena, Jean-Pierre Hubaux

J Med Internet Res 2021;23(2):e25120

Proposal and Assessment of a De-Identification Strategy to Enhance Anonymity of the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) in a Public Cloud-Computing Environment: Anonymization of Medical Data Using Privacy Models

Proposal and Assessment of a De-Identification Strategy to Enhance Anonymity of the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) in a Public Cloud-Computing Environment: Anonymization of Medical Data Using Privacy Models

Hence, to address these concerns, anonymization studies and frameworks are currently being proposed for various datasets [8-10]. As CDM database research does not extract nor analyze the institutional raw data, it involves a low risk of personal information disclosure.

Seungho Jeon, Jeongeun Seo, Sukyoung Kim, Jeongmoon Lee, Jong-Ho Kim, Jang Wook Sohn, Jongsub Moon, Hyung Joon Joo

J Med Internet Res 2020;22(11):e19597

Use and Understanding of Anonymization and De-Identification in the Biomedical Literature: Scoping Review

Use and Understanding of Anonymization and De-Identification in the Biomedical Literature: Scoping Review

Journal Year of publication Author(s) Authors’ backgrounds Authors’ places of work Presence of the terms “de-identification” and “anonymization” Definitions of the terms “de-identification” and “anonymization” Meanings given to the terms “de-identification” and “anonymization” Purposes of de-identification and anonymization Limitations of the privacy-enhancing techniques Ethical or legal considerations Suggestions and recommendations Data utility and information loss Data sharing in biomedical research Types

Raphaël Chevrier, Vasiliki Foufi, Christophe Gaudet-Blavignac, Arnaud Robert, Christian Lovis

J Med Internet Res 2019;21(5):e13484