Trust Management of Social Networks in Health Care

Chomphoosang, Pawat; Durresi, Arjan; Durrësi, Mimoza; Barolli, Leonard

doi:10.1109/nbis.2012.152

Cited by 9 publications

(3 citation statements)

References 26 publications

(17 reference statements)

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“…There are several applications of this framework, such as stock market prediction with Twitter data [37]. Other examples include trust management in social networks [54], cloud computing [34,35], internet of things [36,38], healthcare [8,9], emergency communications [12], and detection of crime [24] and fake users [22].…”

Section: Related Workmentioning

confidence: 99%

Trustworthy Acceptance: A New Metric for Trustworthy Artificial Intelligence Used in Decision Making in Food–Energy–Water Sectors

Uslu

Kaur

Rivera

et al. 2021

Lecture Notes in Networks and Systems

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We propose, for the first time, a trustworthy acceptance metric and its measurement methodology to evaluate the trustworthiness of AI-based systems used in decision making in Food Energy Water (FEW) management. The proposed metric is a significant step forward in the standardization process of AI systems. It is essential to standardize the AI systems' trustworthiness, but until now, the standardization efforts remain at the level of high-level principles. The measurement methodology of the proposed includes human experts in the loop, and it is based on our trust management system. Our metric captures and quantifies the system's transparent

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Section: Related Workmentioning

confidence: 99%

Trustworthy Acceptance: A New Metric for Trustworthy Artificial Intelligence Used in Decision Making in Food–Energy–Water Sectors

Uslu

Kaur

Rivera

et al. 2021

Lecture Notes in Networks and Systems

Self Cite

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“…Trust management is a key technique to provide security and privacy protection of medical information in healthcare social network. A comprehensive overview of different ways of evaluating trustworthiness of health information in healthcare social networks is presented (Chomphoosang, 2012). Two interesting questions for further study are raised: 1) Do online health social networks influence patients differently from physicians, and 2) Does social presence mediate the decision-making influence of online social networks differently for patients than for doctors?…”

Section: Trust-based Access Controlmentioning

confidence: 99%

Issues and Challenges in Securing eHealth Systems

Bai

Dai

2014

International Journal of E-Health and Medical Communications

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With the widespread eHealth usage, security of eHealth services is becoming increasingly important. In this paper, we analyze the security problems in eHealth systems, discuss various approaches of securing health data collection and sharing proposed in the recent literature on eHealth security, and provide comparative evaluations that include advantages and limitations of each approach. Possible future research directions on each approach for enhancing security for eHealth applications are also suggested.

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“…Some of the applications of this framework include stock market analysis using Twitter data, trust management of Internet of Things, fake user detection, and crime predictions. [16,27,23,28,29,30,11,40,41,8,39,7,25,24,6,17].…”

Section: Introductionmentioning

confidence: 99%

Control Theoretical Modeling of Trust-Based Decision Making in Food-Energy-Water Management

Uslu

Kaur

Rivera

et al. 2020

Advances in Intelligent Systems and Computing

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We propose a hybrid Human-Machine decision making to manage Food-Energy-Water resources. In our system trust among human actors during decision making is measured and managed. Furthermore, such trust is used to pressure human actors to chose among the solutions generated by algorithms that satisfy the community's preferred trade-offs among various objectives. We model the trust-based loops in decision making by using control theory. In this system, the feedback information is the trust pressure that actors receive from peers. Using control theory, we studied the dynamics of the trust of an actor. Then, we presented the modeling of the change of solution distances. In both scenarios, we also calculated the settling times and the stability using the transfer functions and their Z-transforms as the number of rounds to show whether and when the decision making is finalized.

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Trust Management of Social Networks in Health Care

Cited by 9 publications

References 26 publications

Trustworthy Acceptance: A New Metric for Trustworthy Artificial Intelligence Used in Decision Making in Food–Energy–Water Sectors

Trustworthy Acceptance: A New Metric for Trustworthy Artificial Intelligence Used in Decision Making in Food–Energy–Water Sectors

Issues and Challenges in Securing eHealth Systems

Control Theoretical Modeling of Trust-Based Decision Making in Food-Energy-Water Management

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