• Journal of Digital Convergence
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• v.15 no.12
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• pp.265-272
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• 2017

As Location-based services on smartphone are getting more popular, users have more concern on exposing their location information. This study developed a research model to identify how smartphone users perceive on providing information pertaining to their location based on privacy calculus theory. 203 responses were analyzed with SmartPLS 2.0. The outcome of this research is quite interesting because conventional belief of privacy calculus perspective does not hold. The privacy calculus theory is based on assumption that human being is rational and decision to provide privacy information is determined by risk and benefit aspects. However, the result of this study is in accordance with behavioral economics perspective in which emotional judgment and behavioral judgement are affected by different factors.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.1
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• pp.69-87
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• 2017

This Research Attempted to Clarify the Eeffect of Privacy Concern and Institutional Trust on Privacy Decision based on Privacy Calculus Perspective. We Developed a Research Model Suggesting that the Influence of Privacy Benefit and Privacy risk on the Information Disclosure Behavior and the Influence of Privacy Concern and Institutional Trust on the Privacy Calculus. In this Regard, in Order to Examine the Difference According to the Target whose Personal Information was Collected, an Empirical Analysis was Conducted to Compare the E-commerce Field and LBS(Location Based Service) Field. The Results of Empirical Analysis are as follows. First, it is Founded that other Relations were All Statistically Significant Except the Relation between Privacy Risk and Information Disclosure Behavior in the LBS group. Next, as a Results of Comparison of Constructs in the E-commerce and Institutional trust than the LBS group, Identifying that the Consumers are more Sensitive to the Personal Information Collected in the E-commerce site.

• Information Systems Review
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• v.24 no.3
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• pp.69-91
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• 2022

The video recommendation services help to save the user's information search time in the overflowing online information, and algorithms for more efficient and accurate recommendation are continuously developed. In particular, TikTok has the largest number of users in the short video industry due to its unique recommendation algorithms. In this study, by applying a privacy calculation model, the research tried to compare users' responses to each type of TikTok's recommendation service. Users are well aware of the privacy concerns and benefits of TikTok's recommendation service. Although there is a risk, it was found that users continue to use TikTok's recommendation service because the benefits are greater.

• Review of KIISC
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• v.15 no.4
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• pp.50-60
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• 2005

인터넷의 발달 및 사회의 정보화는 다양한 업무의 전자적 처리를 가능하게 하여 많은 편리함과 이점을 제공하고 있지만 다양한 보안상의 문제점들이 나타나고 있다. 최근에는 개인의 민감한 정보 노출이 큰 문제로 부각되고 있으며, 이를 기술적으로 해결하고자 하는 노력들이 이루어지고 있다. 본 고에서는 다양한 응용에서 프라이버시 보호에 대한 해결책을 제시할 수 있을 것으로 기대되는 안전한 계산 기술에 대해 전반적으로 소개하고 최근의 관련 연구 동향을 정리한다. 또한 안전한 계산 기술의 실제적인 적용 가능 분야에 대해서 알아보고 프라이버시 문제에 대한 해결책으로서 안전한 계산 기술의 가능성을 살펴본다.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.181-189
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• 2024

The purpose of this study is to investigate the role of interaction privacy management behavior (Use of IPCs), which has received relatively little attention. To this end, this study proposes an integrated model that theorizes the relationship between the main variables of the privacy calculation model and interaction privacy management behavior. Empirical analysis of this model shows that the use of IPCs lowers risks, increases benefits, and in turn promotes increased self-disclosure. These results have implications for expanding the theoretical logic of the privacy calculation model because users' self-disclosure includes not only the limited exposure proposed in the model but also unrestricted exposure through the use of IPC.

• Journal of the Korea Society of Computer and Information
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• v.27 no.1
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• pp.193-200
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• 2022

In this paper, we examined the causes of privacy concerns and related factors in social log-in services. On the basis of the 'principal-agent theory,' we established factors such as perceived information asymmetry and fear of seller opportunism affecting information privacy concern of social log-in services users. In addition, we analyzed the relationship between the information privacy concern and intention to use on the basis of the 'privacy calculus theory'. The results of the study showed that (1) fear of seller opportunism had the significant effect on information privacy concerns, (2) information privacy concerns had the significant effect on perceived risk, (3) in accordance with the privacy calculus theory, perceived risk had the negative effect on intention to use, while perceived benefit had the positive effect on intention to use. The findings of the study are expected to help to improve the social log-in service firms' understanding for customers' information privacy protection behaviors.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.1061-1064
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• 2008

SMC로 불리는 안전한 다자간 계산 프로토콜은 이론적으로 완벽한 프라이버시 보호 기능 및 데이터 정확성을 가지고 있지만 현재의 컴퓨팅 환경에서는 구현이 불가능할 정도로 비효율적이다. 매우 효율적이어서 실용화 되어 있는 랜덤화 기법은 상대적으로 낮은 수준의 프라이버시 보호 기능을 지니고 있다. 최근 SMC와 랜덤화 기법을 적절히 혼합한 형태의 프라이버시 보호 기술이 Teng-Du(2007)에 의해서 제안되었다. 본 논문에서 우리는 Teng-Du의 기법을 면밀히 분석하여 새롭게 구현한 연구 결과를 제시한다. SMC 기술로는 Vaidya-Clifton의 스칼라곱 프로토콜을 채택하고, Agrawal-Jayant-Haritsa가 제안한 랜덤대치 기법을 랜덤화 기술로 선택하여 복합적으로 사용한 프라이버시 보호 기법을 제안한다.

• Knowledge Management Research
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• v.22 no.2
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• pp.33-53
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• 2021

Use of mobile apps being extended, privacy concern on the side of the users is increased while they are willing to provide the private information to use the apps. In this study, we tried to identify the motivating elements that influence the users' intention to use the apps, based on the tendency towards regulatory focus and the privacy calculus theory. To verify the study model, we collected data from 151 adults who use health apps throughout the country, and analyzed the data using the PLS-SEM method. According to the result of the study, it was turned out that tendency towards promotion focus had negative impact on privacy concern and privacy danger, and tendency towards prevention focus had positive impact on privacy concern. Privacy concern had negative impact on the intention to use the mobile apps, and privacy benefit and privacy knowledge had positive impact on the intention to use the mobile apps. Finally, the intention to use the mobile apps had positive impact on the intention to continue to use the mobile apps. In this study, we identified different impacts of two types of tendency towards regulatory focus on privacy concern, and identified different influences on the intention to use the mobile apps accordingly.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.12
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• pp.2695-2701
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• 2012

We need privacy protection protocols, that satisfy three essential security requirements; confidentiality, indistinguishability and forward security, in order to protect user's privacy in RFID system. The hash-chain based protocol that Ohkubo et. al proposed is the most secure protocol, that satisfies all of the essential security requirements, among existing protocols. But, this protocol has a disadvantage that it takes very long time to identify a tag in the back-end server. In this paper, we propose a scheme to keep security just as it is and to reduce computation time for identifying a tag in back-end server. The proposed scheme shows the results that the identification time in back-end server is reduced considerably compared to the hash-chain based protocol.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.8
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• pp.1428-1434
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• 2006

In this paper, we propose the scheme solving privacy infringement in RFID systems with improving the scalability of back-end server. With RFID/USN becoming important subject, many approaches have been proposed and applied. However, limits of RFID, low computation power and storage, make the protection of privacy difficult. The Hash Chain scheme has been known as one guaranteeing forward security, confidentiality and indistinguishability. In spite of that, it is a problem that requires much of computation to identify tags in Back-End server. In this paper, we introduce an efficient key search method, the Hellman Method, to reduce computing complexity in Back-End server. Hellman Method algorism progresses pre-computation and (re)search. In this paper, after applying Hellman Method to Hash chain theory, We compared Preservation and key reference to analyze and apply to parallel With guaranteeing requistes of security for existing privacy protecting Comparing key reference reduced computation time of server to reduce computation complex from O(m) to $O(\frac{m{^2/3}}{w})$ than the existing form.