• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.9
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• pp.3250-3265
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• 2014

Integrated WiMAX and WiFi networks is of great potential for the future due to the wider coverage of WiMAX and the high data transport capacity of WiFi. However, seamless and secure handover (HO) is one of the most challenging issues in this field. In this paper, we present a novel vertical HO authentication scheme with privacy preserving for WiMAX-WiFi heterogeneous networks. Our scheme uses ticket-based and pseudonym-based cryptographic methods to secure HO process and to achieve high efficiency. The formal verification by the AVISPA tool shows that the proposed scheme is secure against various malicious attacks and the simulation result indicates that it outperforms the existing schemes in terms of communication and computation cost.

• Journal of the Korea Society of Computer and Information
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• v.17 no.12
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• pp.251-258
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• 2012

The protection of medical information by major Issues on medical information to protect the individuals' privacy on medical information. Especially, Issues of medical service information, medical record, insurance, employment, Genetic technology including genetic test and screening, gene therapy and genetic enhancement is developing rapidly. Defensibility of medical information documentation is tested in the courts. medical information can be illicitly accessed from anywhere and transmitted across the quickly and with risk of detection. Once data is distributed on the internet, it may become available to anyone who wishes to purchase it, and it cannot be expunge. Patient privacy protection of medical information is controlled mostly by patient consent laws that define how and when a patient must consent before a physician may disclose the patient's medical information to anyone else. enterprise that offers consumers commodities or services is checking problem about customer information of management system is checking problem about customer information of management system essentially. Therefore, in this paper will find a way out to Protection of medical information by major Issues on medical information.

• Information Systems Review
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• v.11 no.2
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• pp.91-111
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• 2009

Collection and use of personal information have enabled firms to improve their value propositions by offering personalization. On the other hand, they have caused widespread concerns by consumers that their privacy is invaded. Because previously distributed personal information can be integrated and utilized to offer more personalized services, those concerns may be intensified with the progress of the digital convergence environment. In this study, factors for privacy invasion and their impact on the privacy concerns are suggested. Then, an empirical study is conducted to examine some of the suggestions. From the analysis, several results are derived. First, as more personal data are collected, secrecy and autonomy concerns increase exponentially rather than steadily. Second, as targeting accuracy is improved, greater secrecy and autonomy concerns are expressed by consumers, but less seclusion concerns. Finally, monetary reward such as coupons can reduce seclusion concerns when targeting accuracy is high. Based on the results, implications for managing consumer privacy concerns are provided under digital convergence environment.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.6
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• pp.197-208
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• 2014

In an information society, privacy protection is one of the most important ethical issues. In medical institutes in which personal medical information is collected and stored, in addition, a privacy breach can cause a serious damage on personal lives. This study attempted to develop privacy concern measurement tool in personal medical information to measure patients' concern on their medical information from medical service consumers' perspective and verify its validity. For this, privacy concern measurement tool in personal medical information was developed based on the results of previous studies. After performing Exploratory Factor Analysis(EFA) and Confirmatory Factor Analysis(CFA) on the measurement tool, its reliability and validity were verified. It appears that the measurement tool would be useful in developing decent privacy protection policy after investigating citizens' concern on the protection of personal medical information and domains they are interested in. For medical institutes as well, they would be helpful in coming up with a reasonable plan after figuring out problems in the protection of personal medical information and current status.

• 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%.

• Journal of Digital Convergence
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• v.17 no.7
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• pp.285-292
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• 2019

With the popularization of PC, SNS and IoT, a lot of data is generated and the amount is increasing exponentially. Artificial neural network learning is a topic that attracts attention in many fields in recent years by using huge amounts of data. Artificial neural network learning has shown tremendous potential in speech recognition and image recognition, and is widely applied to a variety of complex areas such as medical diagnosis, artificial intelligence games, and face recognition. The results of artificial neural networks are accurate enough to surpass real human beings. Despite these many advantages, privacy problems still exist in artificial neural network learning. Learning data for artificial neural network learning includes various information including personal sensitive information, so that privacy can be exposed due to malicious attackers. There is a privacy risk that occurs when an attacker interferes with learning and degrades learning or attacks a model that has completed learning. In this paper, we analyze the attack method of the recently proposed neural network model and its privacy protection method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.4C
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• pp.290-295
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• 2005

When a Certification Authority (CA) issues X.509 public-key certificate to bind a public key to a user, the user is specified through one or more subject name in the 'subject' field and the 'subjectAltName' extension field of a certificate. The 'subject' field or the 'subjectAltName' extension field may contain a hierarchically structured distinguished name, an electronic mail address, If address, or other name forms that correspond to the subject. In this paper, we present the requirements for certificate holder's privacy protection and propose the methods to protect the user's privacy information contained in the 'subject' field or the 'subjectAltName' extension field of a public-key certificat

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2841-2860
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• 2018

Vehicular networks play an important role in current intelligent transportation networks and have gained much attention from academia and industry. Vehicular networks can be enhanced by Long Term Evolution-Vehicle (LTE-V) technology, which has been defined in a series of standards by the 3rd Generation Partnership Project (3GPP). LTE-V technology is a systematic and integrated V2X solution. To guarantee secure LTE-V communication, security and privacy issues must be addressed before the network is deployed. The present study aims to improve the security functionality of vehicular LTE networks by proposing an efficient and secure ID-based message authentication scheme for vehicular networks, named the ESMAV. We demonstrate its ability to simultaneously support both mutual authentication and privacy protection. In addition, the ESMAV exhibit better performance in terms of overhead computation, communication cost, and security functions, which includes privacy preservation and non-frameability.

• Journal of Satellite, Information and Communications
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• v.12 no.1
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• pp.22-27
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• 2017

In this paper, A heightened interest in the latest drone, yet steady increase in the field to exploit them. The next market is expected to be vastly improved, and use drone also increasingly will be strengthened. But also not a few side effects of him. In order to use safer and more commonly a drone safety and privacy protection, and there are many issues that need to be considered. Violating privacy laws, such as relations caused by the drone flight and review this study ways to resolve the breach. The flight due to a draw at peace with human life and behavior and enjoy it, but this problem created by new outbreak.

• Journal of the Korea Convergence Society
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• v.9 no.11
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• pp.75-81
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• 2018

Anonymity and privacy issues are becoming important as all transactions in the blockchain are open to users. Public blockchains appear to guarantee anonymity by using public-key addresses on behalf of users, but they can weaken anonymity by tracking with various analytic techniques based on transaction graph. In this paper, we propose a scheme to protect anonymity and privacy by converging various security techniques such as k-anonymity, mixing, blind signature, multi-phase processing, random selection, and zero-knowledge proof techniques with incentive mechanism and contributor participation. Through performance analysis, our proposed scheme shows that it is difficult to invade privacy and anonymity through collusion attacks if the number of contributors is larger than that of conspirators.