A Comparison of Mobile Patient Monitoring Systems

Paliwal, Gaurav; Kiwelekar, Arvind W.

doi:10.1007/978-3-642-37899-7_17

Cited by 10 publications

(4 citation statements)

References 23 publications

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“…The Home-Index Pseudonym Generation (HIP-Gen) and Linkage (HIP-Lnk) algorithms are executed by HCPsrv by using two symmetric keys (SK 1 H C P , SK 2 H C P ), that are only known to HCPsrv. The HIP-Gen algorithm uses Equations (1) and (2) and the HIP-Lnk algorithm uses Equations (3) and 4, where E/D are, respectively a symmetric encryption/decryption function, such as AES and other parameter values used in these equations (and other equations described in this section) are summarized in Table 2.…”

Section: Home-index Pseudonym Generation and Linkage Algorithmsmentioning

confidence: 99%

“…The collected data may be further processed and used for clinical decision making. These collected data are collectively called Patient Generated Health Data (PGHD) [3,4]. Future MPM systems are anticipated to be built on third-party owned infrastructures with more resourceful storage and data processing capabilities such as those by Microsoft, Amazon, and Google [5].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Home-index Pseudonym Generation and Linkage Algorithmsmentioning

confidence: 99%

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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“…The nature of such data, which carries information that is intrinsically personal, requires par-ticular attention in the process of evaluating the status of anonymity. While the results of generic blood analysis, simply disassociated with the person's identity can be reasonably considered to be anonymous, the same cannot be said in the case of bioimaging, which, in certain circumstances, could permit the identification of the person involved [6,7].…”

Section: Legal Backgroundmentioning

confidence: 99%

Open Data in the Health Context The Lombardy Region Experience

Augello¹,

Barone²,

Crespi³

et al. 2016

ejbi

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“…The operations performed by a PHM system are typically of three stages: data collection, data analysis (i.e., the analysis of the collected data), and decision making (based on the outcome of the data analysis). The data collection stage is crucial to the correct running of a PHM system, as the correctness of the analysis and decision making are dependent on the correctness of the data collected [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Secure, ID Privacy and Inference Threat Prevention Mechanisms for Distributed Systems

Aljohani

Zhang

2023

IEEE Access

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This paper investigates facilitating remote collection of a patient's data in distributed system while protecting the security of the data, preserving the privacy of the patient's ID, and preventing inference attack. The paper presents a novel framework called SPID stand for a Secure, ID Privacy, and Inference Threat Prevention Mechanisms for Distributed Systems. In designing this framework, we make the following novel contributions. The SPID presents a novel architecture that supports the use of a distributed set of servers owned by different service providers. The SPID allows the patient to access these servers using certificates generated by the patient. The SPID allows the patient to select one server to be the home server, and select a number of servers to be the foreign servers. The patient uses the foreign servers to upload data. The home server is responsible for collecting the patient's data from the foreign servers and sending them to the healthcare provider. The SPID proposes a method for efficient verification of each request from the patient without searching in the server's database for the verification key. This is done by using some of the Elliptic Curves Cryptography (ECC) properties. The SPID has been analyzed using a bench-marking tool and evaluated using queuing theory. The evaluation results indicate an efficient performance when the number of servers increases. We uses Shannon entropy method to measure the likelihood of the inference attack.

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A Comparison of Mobile Patient Monitoring Systems

Cited by 10 publications

References 23 publications

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

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

Open Data in the Health Context The Lombardy Region Experience

Secure, ID Privacy and Inference Threat Prevention Mechanisms for Distributed Systems

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