Valuing Personal Data with Privacy Consideration

Li, Xiaobai; Li, Xiaoping; Motiwalla, Luvai

doi:10.1111/deci.12442

Cited by 16 publications

(2 citation statements)

References 61 publications

(79 reference statements)

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“…Personal data is information on an individual's attributes, name, age, gender, education, occupation, address, and family status (Li et al, 2021). Another meaning of "personal data" is private and confidential information in the form of a person's identity, code, symbol, letter, or number (Latumahina, 2014).…”

Section: Theoretical Basismentioning

confidence: 99%

The Urgency of Legal Protection Against the Implementation of Electronic Information Technology-Based Medical Records in Regulation of the Minister of Health of the Republic of Indonesia Number 269 of 2008

Hutabarat¹,

Zebua²,

Sitorus³

et al. 2022

JHSSB

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Indonesia is a country that until now has not have a single law laws specifically regulating the protection of personal data. Lately there have been many cases in the community regarding the misuse of personal data. Especially with the increasingly easy public access to the internet so that the misuse of personal data through electronic media will spread quickly. In the field of health services, the patient's personal data cannot be separated from the possibility of abuse. It would be even more dangerous if the personal data is a trace of the patient's medical record which is indeed highly confidential. The use of Information and Communication Technology (ICT) in health services aims to improve access, efficiency, effectiveness, and quality of medical processes that involve medical service organizations in hospitals, clinics, health centers, medical practitioners both doctors and therapists, laboratories, pharmacies, insurance also involving patients as consumers. However, in the process of service using the E-health program, it will collect a number of consumer personal data which is sensitive personal data and lead to new legal issues, namely the extent to which the health service provider can protect patient personal data can be accessed, disseminated more easily through the progress of ICT. So far, the existing regulations have not provided maximum protection for patient personal data because the arrangements are still sectoral and are scattered in several laws and regulations.

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Section: Theoretical Basismentioning

confidence: 99%

The Urgency of Legal Protection Against the Implementation of Electronic Information Technology-Based Medical Records in Regulation of the Minister of Health of the Republic of Indonesia Number 269 of 2008

Hutabarat¹,

Zebua²,

Sitorus³

et al. 2022

JHSSB

View full text Add to dashboard Cite

show abstract

“…First, we recognize that individuals are not capable of always acting with their own (or collective) interests in mind [ 50 ]. Specifically in their health data sharing attitudes, even though people may be aware of the value of data and potential privacy issues, their actions are often contrasting to their stated beliefs [ 51 , 52 ].…”

Section: Introductionmentioning

confidence: 99%

Comparing Decentralized Learning Methods for Health Data Models to Nondecentralized Alternatives: Protocol for a Systematic Review

Vasconcelos¹,

Souza²,

Rb-Silva³

et al. 2023

JMIR Res Protoc

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Background Considering the soaring health-related costs directed toward a growing, aging, and comorbid population, the health sector needs effective data-driven interventions while managing rising care costs. While health interventions using data mining have become more robust and adopted, they often demand high-quality big data. However, growing privacy concerns have hindered large-scale data sharing. In parallel, recently introduced legal instruments require complex implementations, especially when it comes to biomedical data. New privacy-preserving technologies, such as decentralized learning, make it possible to create health models without mobilizing data sets by using distributed computation principles. Several multinational partnerships, including a recent agreement between the United States and the European Union, are adopting these techniques for next-generation data science. While these approaches are promising, there is no clear and robust evidence synthesis of health care applications. Objective The main aim is to compare the performance among health data models (eg, automated diagnosis and mortality prediction) developed using decentralized learning approaches (eg, federated and blockchain) to those using centralized or local methods. Secondary aims are comparing the privacy compromise and resource use among model architectures. Methods We will conduct a systematic review using the first-ever registered research protocol for this topic following a robust search methodology, including several biomedical and computational databases. This work will compare health data models differing in development architecture, grouping them according to their clinical applications. For reporting purposes, a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 flow diagram will be presented. CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies)–based forms will be used for data extraction and to assess the risk of bias, alongside PROBAST (Prediction Model Risk of Bias Assessment Tool). All effect measures in the original studies will be reported. Results The queries and data extractions are expected to start on February 28, 2023, and end by July 31, 2023. The research protocol was registered with PROSPERO, under the number 393126, on February 3, 2023. With this protocol, we detail how we will conduct the systematic review. With that study, we aim to summarize the progress and findings from state-of-the-art decentralized learning models in health care in comparison to their local and centralized counterparts. Results are expected to clarify the consensuses and heterogeneities reported and help guide the research and development of new robust and sustainable applications to address the health data privacy problem, with applicability in real-world settings. Conclusions We expect to clearly present the status quo of these privacy-preserving technologies in health care. With this robust synthesis of the currently available scientific evidence, the review will inform health technology assessment and evidence-based decisions, from health professionals, data scientists, and policy makers alike. Importantly, it should also guide the development and application of new tools in service of patients’ privacy and future research. Trial Registration PROSPERO 393126; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=393126 International Registered Report Identifier (IRRID) PRR1-10.2196/45823

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What is Your Information Worth? A Systematic Analysis of the Endowment Effect of Different Data Types

Schmitt,

Conde,

Sahitaj

et al. 2023

Lecture Notes in Computer Science

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Valuing Personal Data with Privacy Consideration

Cited by 16 publications

References 61 publications

The Urgency of Legal Protection Against the Implementation of Electronic Information Technology-Based Medical Records in Regulation of the Minister of Health of the Republic of Indonesia Number 269 of 2008

The Urgency of Legal Protection Against the Implementation of Electronic Information Technology-Based Medical Records in Regulation of the Minister of Health of the Republic of Indonesia Number 269 of 2008

Comparing Decentralized Learning Methods for Health Data Models to Nondecentralized Alternatives: Protocol for a Systematic Review

What is Your Information Worth? A Systematic Analysis of the Endowment Effect of Different Data Types

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