Identity Management and Audit Trail Support for Privacy Protection in E-Health Networks

2012 ◽  
pp. 1112-1125
Author(s):  
Liam Peyton ◽  
Jun Hu
Keyword(s):  
Privacy Protection ◽  
Identity Management ◽  
Healthcare Services ◽  
Internet Technology ◽  
Data Interoperability ◽  
Health Networks ◽  
Organizational Policies ◽  
Audit Trail ◽  
Single Sign On ◽  
Reporting Mechanism

E-health networks can enable integrated healthcare services and data interoperability in the form of electronic health records accessible via Internet technology. Efficiency and quality of care can be improved for example by: streamlining administrative processes involving prescriptions and insurance payments; providing remote access to specialists through telemedicine; or correlating data from clinics, pharmacies and emergency rooms to detect potential adverse events. However, a major requirement to enable adoption of e-health networks is the ability to address issues around security, privacy and trust in a systematic manner. In particular, privacy legislation, regulatory guidelines, and organizational policies require that a framework for privacy protection must be established. Federated identity management can be used to systematically protect patient and health care provider identities in a single sign on framework that controls access to patient data, but an audit trail and reporting mechanism is needed in order to ensure and validate compliance. In this chapter, the authors use example e-health scenarios to analyze the legal, business and technical issues that need to be addressed.

Identity Management and Audit Trail Support for Privacy Protection in E-Health Networks

2011 ◽  
pp. 160-173
Author(s):  
Liam Peyton ◽  
Jun Hu
Keyword(s):  
Privacy Protection ◽  
Identity Management ◽  
Healthcare Services ◽  
Internet Technology ◽  
Data Interoperability ◽  
Health Networks ◽  
Organizational Policies ◽  
Audit Trail ◽  
Single Sign On ◽  
Reporting Mechanism

E-health networks can enable integrated healthcare services and data interoperability in the form of electronic health records accessible via Internet technology. Efficiency and quality of care can be improved for example by: streamlining administrative processes involving prescriptions and insurance payments; providing remote access to specialists through telemedicine; or correlating data from clinics, pharmacies and emergency rooms to detect potential adverse events. However, a major requirement to enable adoption of e-health networks is the ability to address issues around security, privacy and trust in a systematic manner. In particular, privacy legislation, regulatory guidelines, and organizational policies require that a framework for privacy protection must be established. Federated identity management can be used to systematically protect patient and health care provider identities in a single sign on framework that controls access to patient data, but an audit trail and reporting mechanism is needed in order to ensure and validate compliance. In this chapter, the authors use example e-health scenarios to analyze the legal, business and technical issues that need to be addressed.

scholarly journals Socioeconomic determinants of unmet need for outpatient healthcare services in Iran: a national cross-sectional study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sajad Vahedi ◽  
Amin Torabipour ◽  
Amirhossein Takian ◽  
Saeed Mohammadpur ◽  
Alireza Olyaeemanesh ◽  
...  
Keyword(s):  
Health System ◽  
Age Groups ◽  
Unmet Need ◽  
Healthcare Services ◽  
Cross Sectional Study ◽  
Outpatient Services ◽  
Self Medication ◽  
Cross Sectional ◽  
Organizational Policies ◽  
Utilization Of Healthcare

Abstract Background Unmet need is a critical indicator of access to healthcare services. Despite concrete evidence about unmet need in Iran’s health system, no recent evidence of this negative outcome is available. This study aimed to measure the subjective unmet need (SUN), the factors associated with it and various reasons behind it in Iran. Methods We used the data of 13,005 respondents over the age of 15 from the Iranian Utilization of Healthcare Services Survey in 2016. SUN was defined as citizens whose needs were not sought through formal healthcare services, while they did not show a history of self-medication. The reasons for SUN were categorized into availability, accessibility, responsibility and acceptability of the health system. The multivariable logistic regression was used to determine significant predictors of SUN and associated major reasons. Results About 17% of the respondents (N = 2217) had unmet need for outpatient services. Nearly 40% of the respondents chose only accessibility, 4% selected only availability, 78% chose only responsibility, and 13% selected only acceptability as the main reasons for their unmet need. Higher outpatient needs was the only factor that significantly increased SUN, responsibility-related SUN and acceptability-related SUN. Low education was associated with higher SUN and responsibility-related SUN, while it could also reduce acceptability-related SUN. While SUN and responsibility-related SUN were prevalent among lower economic quintiles, having a complementary insurance was associated with decreased SUN and responsibility-related SUN. The people with basic insurance had lower chances to face with responsibility-related SUN, while employed individuals were at risk to experience SUN. Although the middle-aged group had higher odds to experience SUN, the responsibility-related SUN were prevalent among elderly, while higher age groups had significant chance to be exposed to acceptability-related SUN. Conclusion It seems that Iran is still suffering from unmet need for outpatient services, most of which emerges from its health system performance. The majority of the unmet health needs could be addressed through improving financial as well as organizational policies. Special attention is needed to address the unmet need among individuals with poor health status.

Modelling online user behavior for medical knowledge learning

2018 ◽  
Vol 118 (4) ◽  
pp. 889-911 ◽  
Author(s):  
Daifeng Li ◽  
Andrew Madden ◽  
Chaochun Liu ◽  
Ying Ding ◽  
Liwei Qian ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Domain Knowledge ◽  
Service Providers ◽  
Healthcare Services ◽  
Internet Technology ◽  
Factor Graph ◽  
Medical Services ◽  
Accurate Analysis ◽  
Content Type ◽  
Medical Domain

Purpose Internet technology allows millions of people to find high quality medical resources online, with the result that personal healthcare and medical services have become one of the fastest growing markets in China. Data relating to healthcare search behavior may provide insights that could lead to better provision of healthcare services. However, discrepancies often arise between terminologies derived from professional medical domain knowledge and the more colloquial terms that users adopt when searching for information about ailments. This can make it difficult to match healthcare queries with doctors’ keywords in online medical searches. The paper aims to discuss these issues. Design/methodology/approach To help address this problem, the authors propose a transfer learning using latent factor graph (TLLFG), which can learn the descriptions of ailments used in internet searches and match them to the most appropriate formal medical keywords. Findings Experiments show that the TLLFG outperforms competing algorithms in incorporating both medical domain knowledge and patient-doctor Q&A data from online services into a unified latent layer capable of bridging the gap between lay enquiries and professionally expressed information sources, and make more accurate analysis of online users’ symptom descriptions. The authors conclude with a brief discussion of some of the ways in which the model may support online applications and connect offline medical services. Practical implications The authors used an online medical searching application to verify the proposed model. The model can bridge users’ long-tailed description with doctors’ formal medical keywords. Online experiments show that TLLFG can significantly improve the searching experience of both users and medical service providers compared with traditional machine learning methods. The research provides a helpful example of the use of domain knowledge to optimize searching or recommendation experiences. Originality/value The authors use transfer learning to map online users’ long-tail queries onto medical domain knowledge, significantly improving the relevance of queries and keywords in a search system reliant on sponsored links.

scholarly journals A Study of Prescriptive Analysis Framework for Human Care Services Based On CKAN Cloud

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Jangwon Gim ◽  
Sukhoon Lee ◽  
Wonkyun Joo
Keyword(s):  
Healthcare Services ◽  
Current Situation ◽  
Sensor Data ◽  
Technological Advancement ◽  
Analysis Framework ◽  
Data Interoperability ◽  
Care Services ◽  
Smart Healthcare ◽  
Future Events ◽  
Prescriptive Analysis

A number of sensor devices are widely distributed and used today owing to the accelerated development of IoT technology. In particular, this technological advancement has allowed users to carry IoT devices with more convenience and efficiency. Based on the IoT sensor data, studies are being actively carried out to recognize the current situation or to analyze and predict future events. However, research for existing smart healthcare services is focused on analyzing users’ behavior from single sensor data and is also focused on analyzing and diagnosing the current situation of the users. Therefore, a method for effectively managing and integrating a large amount of IoT sensor data has become necessary, and a framework considering data interoperability has become necessary. In addition, an analysis framework is needed not only to provide the analysis of the users’ environment and situation from the integrated data, but also to provide guide information to predict future events and to take appropriate action by users. In this paper, we propose a prescriptive analysis framework using a 5W1H method based on CKAN cloud. Through the CKAN cloud environment, IoT sensor data stored in individual CKANs can be integrated based on common concepts. As a result, it is possible to generate an integrated knowledge graph considering interoperability of data, and the underlying data is used as the base data for prescriptive analysis. In addition, the proposed prescriptive analysis framework can diagnose the situation of the users through analysis of user environment information and supports users’ decision making by recommending the possible behavior according to the coming situation of the users. We have verified the applicability of the 5W1H prescriptive analysis framework based on the use case of collecting and analyzing data obtained from various IoT sensors.

scholarly journals k-NDDP: An Efficient Anonymization Model for Social Network Data Release

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2440
Author(s):  
Shafaq Shakeel ◽  
Adeel Anjum ◽  
Alia Asheralieva ◽  
Masoom Alam
Keyword(s):  
Social Networking ◽  
Social Networking Sites ◽  
Privacy Protection ◽  
Differential Privacy ◽  
State Of The Art ◽  
Internet Technology ◽  
Social Data ◽  
Social Network Data ◽  
The Social ◽  
Vertex Degrees

With the evolution of Internet technology, social networking sites have gained a lot of popularity. People make new friends, share their interests, experiences in life, etc. With these activities on social sites, people generate a vast amount of data that is analyzed by third parties for various purposes. As such, publishing social data without protecting an individual’s private or confidential information can be dangerous. To provide privacy protection, this paper proposes a new degree anonymization approach k-NDDP, which extends the concept of k-anonymity and differential privacy based on Node DP for vertex degrees. In particular, this paper considers identity disclosures on social data. If the adversary efficiently obtains background knowledge about the victim’s degree and neighbor connections, it can re-identify its victim from the social data even if the user’s identity is removed. The contribution of this paper is twofold. First, a simple and, at the same time, effective method k–NDDP is proposed. The method is the extension of k-NMF, i.e., the state-of-the-art method to protect against mutual friend attack, to defend against identity disclosures by adding noise to the social data. Second, the achieved privacy using the concept of differential privacy is evaluated. An extensive empirical study shows that for different values of k, the divergence produced by k-NDDP for CC, BW and APL is not more than 0.8%, also added dummy links are 60% less, as compared to k-NMF approach, thereby it validates that the proposed k-NDDP approach provides strong privacy while maintaining the usefulness of data.

scholarly journals Purpose Based Access Control for Privacy Protection in E-Healthcare Services

2012 ◽  
Vol 7 (11) ◽  
Author(s):  
Lili Sun ◽  
Hua Wang ◽  
Jeffrey Soar ◽  
Chunming Rong
Keyword(s):  
Access Control ◽  
Privacy Protection ◽  
Healthcare Services

scholarly journals Trusted Computing Strengthens Cloud Authentication

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Eghbal Ghazizadeh ◽  
Mazdak Zamani ◽  
Jamalul-lail Ab Manan ◽  
Mojtaba Alizadeh
Keyword(s):  
Identity Management ◽  
Trusted Computing ◽  
Security And Privacy ◽  
Trusted Platform Module ◽  
Single Sign On ◽  
Proposed Model ◽  
Security Challenges ◽  
Trusted Platform ◽  
Federated Identity ◽  
New Generation

Cloud computing is a new generation of technology which is designed to provide the commercial necessities, solve the IT management issues, and run the appropriate applications. Another entry on the list of cloud functions which has been handled internally is Identity Access Management (IAM). Companies encounter IAM as security challenges while adopting more technologies became apparent. Trust Multi-tenancy and trusted computing based on a Trusted Platform Module (TPM) are great technologies for solving the trust and security concerns in the cloud identity environment. Single sign-on (SSO) and OpenID have been released to solve security and privacy problems for cloud identity. This paper proposes the use of trusted computing, Federated Identity Management, and OpenID Web SSO to solve identity theft in the cloud. Besides, this proposed model has been simulated in .Net environment. Security analyzing, simulation, and BLP confidential model are three ways to evaluate and analyze our proposed model.

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