• Journal of Korean Academy of Nursing Administration
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• v.23 no.1
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• pp.52-62
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• 2017

Purpose: The aim of this study was to elucidate clinical nurses' ethics germane to information literacy and perception of patient data privacy and thus help nurses to develop more positive and consolidated ethical values. Methods: For this study a descriptive survey design was used. Participants were 142 nurses who worked in a hospital and completed self-report questionnaires. Data were collected from August 1 to 5, 2016 and were analyzed using independent t-test, ANOVA, $Scheff{\acute{e}}$ test, Pearson correlation coefficients, and stepwise multiple regression with SPSS 22.0. Results: Ethical value had a positive correlation with information needs (r=.25, p=.002) in information literacy as well as in direct patient care (r=.27, p=.001), shift work (r=.20, p=.016), patient information management (r=.39, p<.001), and communication (r=.24, p=.004) in perception of patient data privacy. Patient information management, educational background, and age were significant variables predicting the level of ethical values and accounted for 21% of the variance. Conclusion: Ethical values education with particular emphasize on managing patient information should be encouraged for nurses who are younger and have a lower education level. Findings indicate a need for education programs to guide clinical nurses to utilize appropriate information when solving ethical challenges in every day nursing practice.

• Journal of the Korea Society of Computer and Information
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• v.22 no.7
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• pp.55-62
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• 2017

Recently, there has been an increasing need for the sharing of microdata containing information regarding an individual entity. As microdata usually contains sensitive information on an individual, releasing it directly for public use may violate existing privacy requirements. Thus, to avoid the privacy problems that occur through the release of microdata for public use, extensive studies have been conducted in the area of privacy-preserving data publishing (PPDP). The k-anonymity algorithm, which is the most popular method, guarantees that, for each record, there are at least k-1 other records included in the released data that have the same values for a set of quasi-identifier attributes. Given an original table, the corresponding k-anonymous table is obtained by generalizing each record in the table into an indistinguishable group, called the equivalent class, by replacing the specific values of the quasi-identifier attributes with more general values. However, query processing over the anonymized data is a very challenging task, due to generalized attribute values. In particular, the problem becomes more challenging with an equi-join query (which is the most common type of query in data analysis tasks) over k-anonymous tables, since with the generalized attribute values, it is hard to determine whether two records can be joinable. Thus, to address this challenge, in this paper, we develop a novel scheme that is able to effectively perform an equi-join between k-anonymous tables. The experiment results show that, through the proposed method, significant gains in accuracy over using a naive scheme can be achieved.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.4
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• pp.7-17
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• 2019

Machine learning is one of the most accurate techniques to predict and analyze various phenomena. K-means clustering is a kind of machine learning technique that classifies given data into clusters of similar data. Because it is desirable to perform an analysis based on a lot of data for better performance, K-means clustering can be performed in a model with a server that calculates the centroids of the clusters, and a number of clients that provide data to server. However, this model has the problem that if the clients' data are associated with private information, the server can infringe clients' privacy. In this paper, to solve this problem in a model with a number of clients, we propose a privacy-preserving K-means clustering method that can perform machine learning, concealing private information using homomorphic encryption.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.5
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• pp.149-160
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• 2011

Technique based on the notions of anonymity is one of several ways to achieve the goal of privacy and it transforms the original data into the micro data by some group based methods. The first notion of group based method is ${\kappa}$-anonymity, and it is enhanced by the notions of ${\ell}$-diversity and t-closeness. Since there is the natural tradeoff between privacy and data utility, the development of practical anonymization algorithms is not a simple work and there is still no noticeable algorithm which achieves some combined anonymity conditions. In this paper, we provides a comparative analysis of previous anonymity and accuracy measures. Moreover we propose an algorithm to achieve ${\ell}$-diversity by the block merging method from a micro-data achieving ${\kappa}$-anonymity.

• Proceedings of the IEEK Conference
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• summer
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• pp.42-46
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• 2004

Mobile IP [9] was introduced to help the mobile user to be contacted with a single IP address even though the point-of-access changes. However, mobile IP creates the so-called 'triangle routing' which makes the delays for data packets longer, as well as creating unnecessary traffic at the home network of the mobile user. To overcome this, Route Optimization (RO) for mobile IP [1] was proposed, which eliminated the triangle routing phenomenon. But [l] requires that the network protocol stack of all existing hosts to change. Privacy is also another matter to be considered. This paper introduces a scheme called Peer Agent scheme to implement RO for mobile IP without requiring existing hosts to change. Method to preserve location privacy while still enabling RO is also considered.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.1-5
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• 2020

Sustainable computing is a rapidly expanding field of research covering the fields of multidisciplinary engineering. With the rapid adoption of Internet of Things (IoT) devices, issues such as security, privacy, efficiency, and green computing infrastructure are increasing day by day. To achieve a sustainable computing ecosystem for future smart cities, it is important to take into account their entire life cycle from design and manufacturing to recycling and disposal as well as their wider impact on humans and the places around them. The energy efficiency aspects of the computing system range from electronic circuits to applications for systems covering small IoT devices up to large data centers. This editorial focuses on the security, privacy, and efficiency of sustainable computing for future smart cities. This issue accepted 17 articles after a rigorous review process.

• Journal of Korean society of Dental Hygiene
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• v.18 no.4
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• pp.561-570
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• 2018

Objectives: This study explored the perception and practice of privacy protection of some dental hygiene students. Methods: On the basis of survey data from 126 respondents, the correlation between the perception and the practice was analyzed. Also the multiple regression analysis was performed on the variables that affect the practice. Cronbach's ${\alpha}$ of the questionnaire was more than 0.6. The items were scored on 5 points scale or true-false type. Results: The perception of privacy protection was 3.23 points, the law is 0.88 points, and the practice is 3.47 points. The educated students were more perceive than those who did not(p<0.05). The higher the perception, the higher the practice(r=0.230, p<0.01). The practice was influenced by the perception(p<0.05). Conclusions: Dental hygiene students should be educated to perceive and protect the personal and medical information of a patient. Also, an educational institutions need a efforts to protect personal information.

• Annual Conference of KIPS
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• 2020.05a
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• pp.228-230
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• 2020

The emergence of collaborative learning to the public is to tackle the user's privacy issue in centralized learning by bringing the AI models to the data source or client device for training Collaborative learning employs computing and storage resources on the client's device. Thus, it is privacy preserved by design. In harmony, blockchain is also prominent since it does not require an intermediary to process a transaction. However, these approaches are not yet fully ripe to be implemented in the real world, especially for the complex system (several challenges need to be addressed). In this work, we present the performance of collaborative learning and potential use case of blockchain. Further, we discuss privacy issues in the system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3826-3851
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• 2016

With traditional data storage solutions becoming too expensive and cumbersome to support Big Data processing, enterprises are now starting to outsource their data requirements to third parties, such as cloud service providers. However, this outsourced initiative introduces a number of security and privacy concerns. In this paper, homomorphic encryption is suggested as a mechanism to protect the confidentiality and privacy of outsourced data, while at the same time allowing third parties to perform computation on encrypted data. This paper also discusses the challenges of Big Data processing protection and highlights its differences from traditional data protection. Existing works on homomorphic encryption are technically reviewed and compared in terms of their encryption scheme, homomorphism classification, algorithm design, noise management, and security assumption. Finally, this paper discusses the current implementation, challenges, and future direction towards a practical homomorphic encryption scheme for securing outsourced Big Data computation.

• Journal of Communications and Networks
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• v.13 no.2
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• pp.102-112
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• 2011

In this paper, we propose a privacy-preserving emergency call scheme, called PEC, enabling patients in life-threatening emergencies to fast and accurately transmit emergency data to the nearby helpers via mobile healthcare social networks (MHSNs). Once an emergency happens, the personal digital assistant (PDA) of the patient runs the PEC to collect the emergency data including emergency location, patient health record, as well as patient physiological condition. The PEC then generates an emergency call with the emergency data inside and epidemically disseminates it to every user in the patient's neighborhood. If a physician happens to be nearby, the PEC ensures the time used to notify the physician of the emergency is the shortest. We show via theoretical analysis that the PEC is able to provide fine-grained access control on the emergency data, where the access policy is set by patients themselves. Moreover, the PEC can withstandmultiple types of attacks, such as identity theft attack, forgery attack, and collusion attack. We also devise an effective revocation mechanism to make the revocable PEC (rPEC) resistant to inside attacks. In addition, we demonstrate via simulation that the PEC can significantly reduce the response time of emergency care in MHSNs.