Privacy Protection with Pseudonymization and Anonymization In a Health IoT System: Results from OCARIoT

Ribeiro, Sérgio; Nakamura, Emilio Tissato

doi:10.1109/bibe.2019.00169

Cited by 19 publications

(6 citation statements)

References 1 publication

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“…The healthcare solutions proposed by developers and service providers should be carefully considered so that only necessary information is being collected, not an excessive amount of data. By adhering to the Keeping Privacy by Design (PbD) [32] principles from the outset, developers could provide solutions that minimize the number of potential risks. Currently, there is a lack of trust layers or trusted management systems for resource exchanges between IoT devices, edge devices, and cloud servers [37].…”

Section: Discussionmentioning

confidence: 99%

“…Pseudonymization and anonymization techniques are used as an added security layer in a health IoT application to eliminate the possibility of identifying an individual after a data breach [32]. In this scenario, aside from the technical aspects, the legal framework is also required to handle the concerns of all stakeholders involved.…”

Section: Privacy Preservation Iot Based Healthcare Solutionsmentioning

confidence: 99%

“…Table 2 lists the prevalent privacy-preserving solutions in healthcare edge computing, along with the limitations that need to be addressed. Quantum level computations SIMD-SHE [21] Dependence on other platforms (cloud computing) Complete or partial use of blockchain [4] Need excessive computation resources Data encryption schemes [15,22] Low computation resources and high latency Cryptography mechanisms [15,28] [29] Limited resources Data pseudonymization and anonymization schemes [30][31][32] Low bandwidth and network performance, absence of legal framework Secure authentication scheme (SAB-UAS) [33] Low latency and network performance with high cost Table 2: Privacy-preserving solutions in edge computing-based healthcare and the associated challenges.…”

Section: Privacy Preservation Edge Computing-based Healthcare Solutionsmentioning

confidence: 99%

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Analysis of Privacy-Preserving Edge Computing and Internet of Things Models in Healthcare Domain

Almusallam

Alabdulatif

Alarfaj

2021

Computational and Mathematical Methods in Medicine

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The healthcare sector is rapidly being transformed to one that operates in new computing environments. With researchers increasingly committed to finding and expanding healthcare solutions to include the Internet of Things (IoT) and edge computing, there is a need to monitor more closely than ever the data being collected, shared, processed, and stored. The advent of cloud, IoT, and edge computing paradigms poses huge risks towards the privacy of data, especially, in the healthcare environment. However, there is a lack of comprehensive research focused on seeking efficient and effective solutions that ensure data privacy in the healthcare domain. The data being collected and processed by healthcare applications is sensitive, and its manipulation by malicious actors can have catastrophic repercussions. This paper discusses the current landscape of privacy-preservation solutions in IoT and edge healthcare applications. It describes the common techniques adopted by researchers to integrate privacy in their healthcare solutions. Furthermore, the paper discusses the limitations of these solutions in terms of their technical complexity, effectiveness, and sustainability. The paper closes with a summary and discussion of the challenges of safeguarding privacy in IoT and edge healthcare solutions which need to be resolved for future applications.

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Section: Discussionmentioning

confidence: 99%

Section: Privacy Preservation Iot Based Healthcare Solutionsmentioning

confidence: 99%

Section: Privacy Preservation Edge Computing-based Healthcare Solutionsmentioning

confidence: 99%

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Analysis of Privacy-Preserving Edge Computing and Internet of Things Models in Healthcare Domain

Almusallam

Alabdulatif

Alarfaj

2021

Computational and Mathematical Methods in Medicine

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“…Regarding information security, the complete anonymization [49] of the data is adequate, however, for the clustering algorithm to be useful in the future, it is necessary to collect additional data and habits to analyze the correlation with the levels of each person's glucose.…”

Section: Discussionmentioning

confidence: 99%

Proposal for a Methodology to Create an Artificial Pancreas for the Control of Type 1 Diabetes using Machine Learning and Automated Insulin Dosing Systems (AID)

Vilanova¹,

Cerqueira²

2021

Natural Language Processing

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The number of children, adolescents, and adults living with diabetes increases annually due to the lack of physical activity, poor diet habits, stress, and genetic factors, and there are greater numbers in low-income countries. Therefore, the aim of this article is to present a proposal for a methodology for developing a pancreas using artificial intelligence to control the required doses of insulin for a patient with type 1 diabetes (T1D), according to data received from monitoring sensors. The information collected can be used by physicians to make medication changes and improve patients’ glucose control using insulin pumps for optimum performance. Therefore, using the model proposed in this work, the patient is offered a gain in glucose control and, therefore, an improvement in quality of life, as well as in the costs related to hospitalization.

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“…Another anonymisation approach is character masking, where some characters are replaced with a wild character such as an asterisk (*). Pseudonymisation is yet another anonymisation approach, where some or all characters are replaced with other values (Ribeiro & Nakamura, 2019). Then there is also generalisation, where data precision is reduced to achieve anonymisation.…”

Section: Despitementioning

confidence: 99%

Assessing Identity Disclosure Risk in the Absence of Identified Datasets in the Public Domain

Muturi

Kahonge

Chepken

2022

East Afr. j. inf. technol.

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Data release is essential in supporting data analytics and secondary data analyses. However, data curators need to ensure the released datasets preserve data subjects’ privacy and retain analytical utility. Data privacy is achieved through the anonymisation of datasets before release. The risk of disclosure posed to the dataset should inform the level of anonymisation to be undertaken. As anonymisation achieves data privacy, it reduces the analytical utility of the dataset by introducing alterations to the original data values. Therefore, data curators require an appropriate estimate of the dataset’s identity disclosure risk to inform the required anonymisation that balances privacy and utility. The disclosure risk varies from one geographical region to another due to varying enabling factors. This paper assesses the disclosure risk and the enabling factors in an environment lacking identified datasets in the public domain. This study used a quasi-experimental design in carrying out an empirical identity disclosure test, where respondents were given an anonymised dataset and were required to disclose the identity of any of the records. The findings were that background knowledge of the released datasets was the primary enabler in the absence of identified datasets. Respondents could only disclose records in the dataset they had familiarity with. However, the disclosure risk was within an acceptable threshold. Therefore, the study concluded that in an environment lacking identified datasets in the public domain, reasonable anonymisation could achieve a balance of privacy and utility in datasets. The findings justify private data release able to support data analytics and secondary data analyses in environments lacking identified datasets in the public domain.

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Privacy Protection with Pseudonymization and Anonymization In a Health IoT System: Results from OCARIoT

Cited by 19 publications

References 1 publication

Analysis of Privacy-Preserving Edge Computing and Internet of Things Models in Healthcare Domain

Analysis of Privacy-Preserving Edge Computing and Internet of Things Models in Healthcare Domain

Proposal for a Methodology to Create an Artificial Pancreas for the Control of Type 1 Diabetes using Machine Learning and Automated Insulin Dosing Systems (AID)

Assessing Identity Disclosure Risk in the Absence of Identified Datasets in the Public Domain

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