Identity-Based Privacy Preservation Framework over u-Healthcare System

Mtonga, Kambombo; Yang, Haomiao; Yoon, Eun‐Jun; Kim, Hyunsung

doi:10.1007/978-94-007-6738-6_26

Cited by 5 publications

(8 citation statements)

References 6 publications

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“…The privacy preservation in health care systems has been enforced using the user identity [9]. The method uses cryptographic techniques which are performed according to the user identity.…”

Section: Majamal Mohamed Yaseen Zubeir Ar Mohamed Shanavasmentioning

confidence: 99%

Real Time Feature Convergence Measure for Efficient Discrimination for Transactional Data Set

Zubeir*,

Shanavas

2019

IJRTE

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The problem of discrimination in transactional data set has been well studied. Numerous techniques has been recommended by various researchers but suffer to achieve higher performance. To handle this issue, a real time feature convergence measure based discrimination prevention algorithm is presented in this paper. The method first eliminates the noisy records by preprocessing the transactional data set. Second, the transactional data set has been grouped into number of clusters according to the pattern relevancy measure (PRM). Using the clusters generated, the the feature convergence measure (FCM) is computed for each item towards each cluster. The value of FCM is used to select a subset of items as sensitive one. Based on identified sensitive items, the method performs sanitization using probabilistic mapping scheme. The FCM algorithm supports the performance development of sanitization and discrimination prevention.

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“…The privacy preservation in health care systems has been enforced using the user identity [9]. The method uses cryptographic techniques which are performed according to the user identity.…”

Section: Majamal Mohamed Yaseen Zubeir Ar Mohamed Shanavasmentioning

confidence: 99%

Real Time Feature Convergence Measure for Efficient Discrimination for Transactional Data Set

Zubeir*,

Shanavas

2019

IJRTE

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“…Existing data collection systems can largely be classified into two groups, centralized and distributed systems. In the centralized data collection systems [12][13][14][15][16][17], patients upload their health data onto a centralized data collection server where data are accessed by a healthcare provider. These systems typically rely on the use of a single entity (a single server or a single architectural component) to perform the tasks required to facilitate data collections.…”

Section: Related Workmentioning

confidence: 99%

“…These concerns include breach of data confidentiality and authenticity and unauthorized exposure of patients' real IDs, thus compromising their privacy. Existing data collection systems [7][8][9][10][11][12][13][14][15][16][17] do protect data confidentiality and preserve patients' ID privacy. However, there are limitations in these systems and proposals in terms of supporting scalability and protection against inference of patients' real IDs via observing their communication patterns and against privacy threats imposed by authorized insiders.…”

Section: Introductionmentioning

confidence: 99%

Designing a Secure and Privacy-Preserving Data Collection (SPDC) Framework for Collecting Data from Mobile Patients

Aljohani¹,

Zhang²

2019

EasyChair Preprints

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Third-party based mobile health monitoring systems are vulnerable to threats not only imposed by outsiders but also authorized insiders, e.g. employees of the third-party service provider. This paper examines issues in this context and proposes a novel framework, called a secure and ID privacy preserving framework (SPDC), to facilitate secure and ID privacy-preserving data collections from remote patients. The framework has a number of properties: (i) it supports three different modes of data collections and different treatment of data with two levels of urgency, (ii) it protects the confidentiality  and authenticity of data being collected, (iii) it preserves patients' ID privacy against external entities as well as entities that are involved in facilitating the data collections, (iv) it uses distributed load-sharing so that no single entity is wholly responsible for the task of data collections. The ideas used in achieving these properties include the use of  structured use of multiple data collection servers  and multi-level hierarchical pseudonyms to hide patients' IDs as well as their communication patterns, and the separation of duties and pseudonym and blind-token based authentication methods so that patients' can be identified and authenticated without revealing their real IDs. Security analysis against design requirements and anonymity evaluation using entropy method are carried out to demonstrate that the framework can resists attacks on data security and protects the patients' IDs from being revealed to unauthorized entities.

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“…However, a disadvantage is that there are limited places to use such a network. On the other hand, there is no spatial limitation on wireless network [28]. However, malicious users are able to steal wireless signals; hence, there are security threats.…”

Section: Sensing Techniquementioning

confidence: 99%

The Design and Analysis of a Secure Personal Healthcare System Based on Certificates

et al. 2016

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Due to the development of information technology (IT), it has been applied to various fields such as the smart home, medicine, healthcare, and the smart car. For these fields, IT has been providing continuous prevention and management, including health conditions beyond the mere prevention of disease, improving the quality of life. e-Healthcare is a health management and medical service to provide prevention, diagnosis, treatment, and the follow-up management of diseases at any time and place in connection with information communication technology, without requiring patients to visit hospitals. However, e-Healthcare has been exposed to eavesdropping, manipulation, and the forgery of information that is personal, biological, medical, etc., and is a security threat from malicious attackers. This study suggests a security service model to exchange personal health records (PHRs) for e-Healthcare environments. To be specific, this study suggests a scheme in which communicators are able to securely authorize and establish security channels by constituting the infrastructure each organization relies on. In addition, the possibility of establishing a security service model is indicated by suggesting an e-Healthcare system for a secure e-Healthcare environment as a secure personal health record system. This is anticipated to provide securer communication in e-Healthcare environments in the future through the scheme suggested in this study.

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Identity-Based Privacy Preservation Framework over u-Healthcare System

Cited by 5 publications

References 6 publications

Real Time Feature Convergence Measure for Efficient Discrimination for Transactional Data Set

Real Time Feature Convergence Measure for Efficient Discrimination for Transactional Data Set

Designing a Secure and Privacy-Preserving Data Collection (SPDC) Framework for Collecting Data from Mobile Patients

The Design and Analysis of a Secure Personal Healthcare System Based on Certificates

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