Impact of Privacy Issues on User Behavioural Acceptance of Personalized mHealth Services

Premarathne, Uthpala Subodhani; Han, Fenghui; Liu, Haibin; Khalil, Ibrahim

doi:10.1007/978-3-319-12817-7_45

Cited by 5 publications

(6 citation statements)

References 52 publications

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“…In this scenario, a novel contribution of the paper is represented by a comprehensive solution aimed at preserving location privacy of individuals through artificial perturbations of location information collected by sensing technology. In particular, location information of users is managed by a trusted middleware [5,6,9], which enforces users privacy through obfuscation-based techniques.…”

Section: A Location Obfuscationmentioning

confidence: 99%

“…This information can be subsequently be accessed by the same or other sectors without the user's permission. The availability of this data allows users' activities, preferences, health and identity to become characterisable, traceable and in some cases uniquely identifiable [5]. One study showed that 87% of mobile users can be uniquely identified, including their postcode, age and gender, using a collection of non-identity attributes [6].…”

Section: Introductionmentioning

confidence: 99%

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Trading-off location accuracy and service quality: Privacy concerns and user profiles

Gardner

Leibovici

Basiri

et al. 2017

2017 International Conference on Localization and GNSS (ICL-GNSS)

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Location privacy has become a growing concern impeding the adoption of many Location Based Services (LBS). Although there have been several approaches, such as anonymisation or obfuscation of location data, none has yet been completely successful at addressing privacy protection. This paper discusses the results of 256 survey responses which show that users' demands, expectations and concerns vary significantly among different user groups (by age, education, income, technological experience and social media activity) and infer that there is no 'one fit for all' solution for different LBS applications due to the variation in use.

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Section: A Location Obfuscationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Trading-off location accuracy and service quality: Privacy concerns and user profiles

Gardner

Leibovici

Basiri

et al. 2017

2017 International Conference on Localization and GNSS (ICL-GNSS)

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“…A driver behind inferring medical specialties is the analysis of audit logs for security and privacy purposes [6,8,24]. This is feasible because EHRs and their audit logs encode valuable interactions between users and patients [26].…”

Section: Related Workmentioning

confidence: 99%

Discovering de facto diagnosis specialties

Zhang

Gunter

et al. 2015

Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics

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In health care institutions, medical specialty information may be lacking or inaccurate, in part because there is no official code to express such specialties. Diagnosis histories offer information on which medical specialties may exist in practice, regardless of whether they have official codes. We refer to such specialties that are predicted with high certainty by diagnosis histories de facto diagnosis specialties. The objective of our research is to discover de facto diagnosis specialties under a general discovery-evaluation framework. Specifically, we employ a semi-supervised learning model (based on heterogeneous information network analysis) and an unsupervised learning method (based on topic modeling) for discovery. We further employ four supervised learning models for evaluation. We use one year of diagnosis histories from a major medical center, which consists of two data sets. One is fine-grained and has diagnoses assigned to 41,603 patients that are accessed by 2,504 medical service providers. The other is general and has diagnoses assigned to 291,562 patients that are accessed by 3,269 medical service providers. The semi-supervised learning model discovers a specialty for Breast Cancer on the fine-grained data set; while the unsupervised learning method confirms this discovery and suggests another specialty for Obesity on the larger general data set. The evaluation results reinforce that these two specialties can be recognized accurately by supervised learning models in comparison with 12 common diagnosis specialties defined by the Health Care Provider Taxonomy Code Set.

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“…[1] Matzner argued that individuals can be harmed even when none of their data has been collected or processed. [2] Researchers believed that privacy concerns can reduce patient interest in wireless sensor networks, [3] electronic records, [4] mobile wellness programs, [5] online health communities, [6] and health care information exchanges. [7] Patient privacy concerns were considered to be the biggest impediment to E-healthcare.…”

Section: Introductionmentioning

confidence: 99%

Including Stakeholders When Implementing New Technologies

Larson

2020

APJHM

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Objective: Technologies such as Radio Frequency Identification (RFID) offer many benefits to health care providers and may raise stakeholder concerns. This study reviews a new technology from another industry, summarizes previous research on medical applications of RFID, and analyses survey responses on RFID applications. The goal is to develop principles for evaluating and implementing new technologies. Design: Marketing and stakeholder theories were used to develop lessons from the case study and from prior research. A survey was mailed to adults in four Midwestern states in the US. Main Outcome Measures: Respondent support ratings for two medical and two non-medical applications of RFID were analysed using principal component analysis and binary logistic regressions. Profiles of those supporting the applications were developed. Results: The case study highlighted the importance of considering the needs and concerns of all stakeholders. Previous studies suggested that many hospital administrators who examined RFID may not have included some stakeholders. This research found that support for RFID varied across respondents and across applications. Anxiety about technology was negatively linked with RFID support. Religiosity also had negative coefficients for some applications. Conclusions: Administrators considering new technologies need to consider patient privacy needs and stakeholder concerns. Surveying stakeholders and utilizing advisory boards could help administrators tailor their actions to the communities they serve. A few opponents to a technology can limit its adoption. Regular, two-way communications may help health care organizations improve technology decisions and enhance the odds of implementation success.

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Impact of Privacy Issues on User Behavioural Acceptance of Personalized mHealth Services

Cited by 5 publications

References 52 publications

Trading-off location accuracy and service quality: Privacy concerns and user profiles

Trading-off location accuracy and service quality: Privacy concerns and user profiles

Discovering de facto diagnosis specialties

Including Stakeholders When Implementing New Technologies

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