• Journal of the Korea Society of Computer and Information
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• v.24 no.9
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• pp.43-49
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• 2019

Today, with the development of the internet of things, wearable devices related to personal health care have become widespread. Various global information and communication technology companies are developing various wearable health devices, which can collect personal health information such as heart rate, steps, and calories, using sensors built into the device. However, since individual health data includes sensitive information, the collection of irrelevant health data can lead to personal privacy issue. Therefore, there is a growing need to develop technology for collecting sensitive health data from wearable health devices, while preserving privacy. In recent years, local differential privacy (LDP), which enables sensitive data collection while preserving privacy, has attracted much attention. In this paper, we develop a technology for collecting vast amount of health data from a smartwatch device, which is one of popular wearable health devices, using local difference privacy. Experiment results with real data show that the proposed method is able to effectively collect sensitive health data from smartwatch users, while preserving privacy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3852-3864
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• 2016

Patients' health data is very sensitive and the access to individual's health data should be strictly restricted. However, many data consumers may need to use the aggregated health data. For example, the insurance companies needs to use this data to setup the premium level for health insurances. Therefore, privacy-preserving data aggregation solutions for health data have both theoretical importance and application potentials. In this paper, we propose a privacy-preserving health data aggregation scheme using differential privacy. In our scheme, patients' health data are aggregated by the local healthcare center before it is used by data comsumers, and this prevents individual's data from being leaked. Moreover, compared with the existing schemes in the literature, our work enjoys two additional benefits: 1) it not only resists many well known attacks in the open wireless networks, but also achieves the resilience against the human-factor-aware differential aggregation attack; 2) no trusted third party is employed in our proposed scheme, hence it achieves the robustness property and it does not suffer the single point failure problem.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.153-160
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• 2024

Information technology plays an important role in healthcare. The cloud has several applications in the fields of education, social media and medicine. But the advantage of the cloud for medical reasons is very appropriate, especially given the large volume of data generated by healthcare organizations. As in increasingly health organizations adopting towards electronic health records in the cloud which can be accessed around the world for various health issues regarding references, healthcare educational research and etc. Cloud computing has many advantages, such as "flexibility, cost and energy savings, resource sharing and rapid deployment". However, despite the significant benefits of using the cloud computing for health IT, data security, privacy, reliability, integration and portability are some of the main challenges and obstacles for its implementation. Health data are highly confidential records that should not be made available to unauthorized persons to protect the security of patient information. In this paper, we discuss the privacy and security requirement of EHS as well as privacy and security issues of EHS and also focus on a comprehensive review of the current and existing literature on Electronic health that uses a variety of approaches and procedures to handle security and privacy issues. The strengths and weaknesses of some of these methods were mentioned. The significance of security issues in the cloud computing environment is a challenge.

• Journal of Korean Clinical Health Science
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• v.2 no.1
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• pp.25-34
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• 2014

Purpose. The checked of perception for the protection of personal medical information of EMT student and Nursing student. Methods. Nursing students and EMT students 200 questionnaires were collected and Frequency analysis, Chi-square test, one-way ANOVA was performed for using the Windows SPSS(ver. 12.0). Results. Most of the subjects were aware of the protection law of personal information and Infringement of the privacy of personal information will be exposed. also, Education is needed privacy(EMT students $3.84{\pm}0.96$, Nursing students $3.73{\pm}0.99$). EMT($3.99{\pm}1.00$) and Nursing($4.07{\pm}0.94$)students due to exposure to both the patient's personal information privacy was violated would get recognized. Exposure to the computerization of information privacy will be exploited in other agencies(EMT students $3.78{\pm}0.88$, Nursing students $3.95{\pm}0.94$) was called. Conclusions. For the protection of personal health information, education needs to be expanded.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.113-121
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• 2020

With the rapid development of information and communication technology (ICT), various sensors are being embedded in wearable devices. Consequently, these devices can continuously collect data including health data from individuals. The collected health data can be used not only for healthcare services but also for analyzing an individual's lifestyle by combining with other external data. This helps in making an individual's life more convenient and healthier. However, collecting health data may lead to privacy issues since the data is personal, and can reveal sensitive insights about the individual. Thus, in this paper, we present a method to collect an individual's health data from a smart band in a privacy-preserving manner. We leverage the local differential privacy to achieve our goal. Additionally, we propose a way to find feature points from health data. This allows for an effective trade-off between the degree of privacy and accuracy. We carry out experiments to demonstrate the effectiveness of our proposed approach and the results show that, with the proposed method, the error rate can be reduced upto 77%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.9
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• pp.2589-2604
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• 2024

Brain tumors, characterized by uncontrollable cellular growths, are a significant global health challenge. Navigating the complexities of tumor identification due to their varied dimensions and positions, our research introduces enhanced methods for precise detection. Utilizing advanced learning techniques, we've improved early identification by preprocessing clinical dataset-derived images, augmenting them via a Generative Adversarial Network, and applying an Improved Privacy-Preserving Collaborative Convolutional Neural Network (IPC-CNN) for segmentation. Recognizing the critical importance of data security in today's digital era, our framework emphasizes the preservation of patient privacy. We evaluated the performance of our proposed model on the Figshare and BRATS 2018 datasets. By facilitating a collaborative model training environment across multiple healthcare institutions, we harness the power of distributed computing to securely aggregate model updates, ensuring individual data protection while leveraging collective expertise. Our IPC-CNN model achieved an accuracy of 99.40%, marking a notable advancement in brain tumor classification and offering invaluable insights for both the medical imaging and machine learning communities.

• Asia pacific journal of information systems
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• v.34 no.2
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• pp.541-564
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• 2024

Contact-tracing applications have significantly contributed to mitigating the spread of coronavirus disease 2019 (COVID-19), yet the extensive use of these location-based applications raises serious privacy concerns. Drawing on the Information-Motivation-Behavioral (IMB) skills model, our study investigated factors that influence users' protective behaviors toward location privacy, elucidating the privacy paradox and the mediating role of implementation intention. Through an online survey conducted in China with 311 participants, we found that privacy concerns and privacy awareness positively affected the use of mobile location service settings, with privacy concerns mediating the relationship between privacy awareness and the intention to protect privacy. Furthermore, our study demonstrated the privacy paradox, revealing the pivotal mediating role of implementation intentions in bridging the gap between users' intentions and their actual behaviors. This study offers new perspectives on the privacy paradox, particularly through the lens of implementation intention, and provides valuable insights for motivating greater use of contact-tracing applications. It offers both theoretical and practical guidance for stakeholders to address privacy concerns during global pandemics like COVID-19, thereby encouraging a more widespread and responsible engagement with technology in public health.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.364-374
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• 2022

Health information systems (HIS) are facing security challenges on data privacy and confidentiality. These challenges are based on centralized system architecture creating a target for malicious attacks. Blockchain technology has emerged as a trending technology with the potential to improve data security. Despite the effectiveness of this technology, still HIS are suffering from a lack of data privacy and confidentiality. This paper presents a blockchain-based data storage security architecture integrated with an e-Health care system to improve its security. The study employed a qualitative research method where data were collected using interviews and document analysis. Execute-order-validate Fabric's storage security architecture was implemented through private data collection, which is the combination of the actual private data stored in a private state, and a hash of that private data to guarantee data privacy. The key findings of this research show that data privacy and confidentiality are attained through a private data policy. Network peers are decentralized with blockchain only for hash storage to avoid storage challenges. Cost-effectiveness is achieved through data storage within a database of a Hyperledger Fabric. The overall performance of Fabric is higher than Ethereum. Ethereum's low performance is due to its execute-validate architecture which has high computation power with transaction inconsistencies. E-Health care system administrators should be trained and engaged with blockchain architectural designs for health data storage security. Health policymakers should be aware of blockchain technology and make use of the findings. The scientific contribution of this study is based on; cost-effectiveness of secured data storage, the use of hashes of network data stored in each node, and low energy consumption of Fabric leading to high performance.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.1-12
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• 2022

Besides unexpected growth perceived by IoT's, the variety and volume of threats have increased tremendously, making it a necessity to introduce intrusion detections systems for prevention and detection of such threats. But Intrusion Detection and Prevention System (IDPS) inside the IoT network yet introduces some unique challenges due to their unique characteristics, such as privacy inference, performance, and detection rate and their frequency in the dynamic networks. Our research is focused on the privacy inferences of existing intrusion prevention and detection system approaches. We also tackle the problem of providing unified a solution to implement the open-source IDPS in the IoT architecture for assessing the performance of IDS by calculating; usage consumption and detection rate. The proposed scheme is considered to help implement the human health monitoring system in IoT networks

• Journal of Internet Computing and Services
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• v.10 no.2
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• pp.29-40
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• 2009

The development of information technology such as the internet has brought about rapidly changes the old medical technology, e-Healthcare has been to raise social issue. The e-Healthcare which new turning point of paradigm in the medical information develops the medical policy in Korea and the technology, the prospective of reverse engineering in internet environment is incurring problems such as distribution of critical information and invasion and infringement of privacy, etc. In this research, we suggest the Role Based Access Control System, HPIP-e-Healthcare Privacy Information Protection, for solving above problem. The HPIP is composed 4 mechanisms such as Consolidate User Identity, Hospital Authorization, Medical Record Access Control, Patient Diagnosis and we are also prototyping the HPIP for feasible approach in the real computing environment.