• 디지털융복합연구
• /
• 제15권12호
• /
• pp.265-272
• /
• 2017

스마트폰의 위치기반서비스가 활발히 사용됨에 따라 사용자들이 자신의 위치정보의 노출에 대한 염려가 커지고 있다. 본 연구는 프라이버시 계산 이론에 기반하여 스마트폰 사용자들이 자신의 위치정보의 제공을 어떻게 받아들이는지 확인하고자 연구모형을 개발하였다. 대학생을 대상으로 수집한 데이터를 SmartPLS를 이용하여 분석한 결과 프라이버시 계산 관점의 기존 연구와는 다른 결과를 얻었다. 프라이버시 계산이론은 인간은 합리적인 존재이며, 위험과 효과 측면을 고려하여 프라이버시 정보의 제공을 결정한다고 본다. 본 연구의 결과는 프라이버시 염려는 위험에 유의하게 영향을 미치나, 사용의도는 효과에 의해서 유의하게 영향을 미쳤다. 이러한 결과는 감정적 판단과 행동적 판단이 서로 상이한 요인에 의해서 영향을 받는다는 행동경제학적 관점과 일치한다.

• 유통과학연구
• /
• 제22권6호
• /
• pp.95-106
• /
• 2024

Purpose: This study investigates the complex dynamics of consumer behavior in Vietnamese banking omnichannel environments, focusing on the roles of service consistency, service transparency, flow, perceived privacy risk, and loyalty intention. Research design, data and methodology: Using a sample of 422 Vietnamese bank customers, data analysis revealed significant relationships among the variables under investigation. Results: Firstly, service consistency was found to positively influence flow experiences and negatively impact perceived privacy risk, highlighting the importance of uniform service quality across channels in enhancing consumer engagement while mitigating privacy concerns. Similarly, service transparency was positively associated with flow experiences and negatively associated with perceived privacy risk, underscoring the importance of transparent information dissemination in fostering immersive consumer experiences while alleviating privacy apprehensions. Furthermore, both flow experiences and perceived privacy risk significantly influenced loyalty intentions, indicating the pivotal roles of engaging experiences and data security in driving consumer loyalty. Additionally, mediated relationships were observed, demonstrating the interplay between service consistency, service transparency, flow, perceived privacy risk, and loyalty intention in shaping consumer behavior in omnichannel contexts. Conclusions: These findings provide valuable insights for retailers and marketers seeking to optimize consumer experiences and cultivate loyalty in omnichannel environments by prioritizing consistency, transparency, and data privacy protection.

• 정보화정책
• /
• 제27권1호
• /
• pp.72-91
• /
• 2020

공개된 프로필을 기반으로 개인정보에 대한 접근이 상대적으로 자유로운 개방형 SNS와 미리 소통할 지인의 범위를 제한하여 개인정보의 유출에 덜 취약한 폐쇄형 SNS에서 프라이버시 역설 현상이 다르게 관측될 수 있다는 개연성에 기인하여, 본 연구에서는 SNS를 개방형과 폐쇄형으로 구분하고, 어떠한 SNS 활동에서 프라이버시 역설이 성립하는지를 살펴보았다. 실증분석의 결과는 다음과 같다. 첫째, SNS 사용 여부의 경우, 전체적인 SNS 활동에서는 프라이버시 역설이 성립하지만, 개방형 및 폐쇄형 SNS에 따라 다른 결론이 도출되었다. 특히, 침해에 더 취약한 개방형 SNS에서는 프라이버시 염려가 증가함에 따라 개인들이 합리적이고 바람직하게 SNS 활동을 감소키는 식으로 대응하는 것이 관찰되었다. 둘째, SNS 사용 강도의 경우, (i) 헤비 유저가 라이트 유저에 비해 프라이버시 침해의 개연성을 더 심각하게 인식하여 프라이버시 염려의 증가에 따라 사용 강도를 감소하는 식의 합리적인 반응을 하며, 또한 (ii) 이러한 경향이 개인정보 침해에 취약한 개방형 SNS에서 더욱 뚜렷하게 관찰되는 것으로 나타났다. 이에 따라 SNS 활동을 구분하지 않고 통합적으로 분석하여 프라이버시 역설의 존재를 주장하는 것은 일종의 '구성의 오류(Fallacy of Composition)'를 범하는 것으로 해석할 수 있다.

• 컴퓨터교육학회논문지
• /
• 제18권1호
• /
• pp.69-79
• /
• 2015

본 연구는 인터넷 사용자를 대상으로 정보보호 활동을 유도할 수 있는 선행 요인들을 실증적으로 탐색하기 위하여 수행되었다. 본 연구에서 제시된 인과모형은 정보프라이버시에 대한 염려가 어떻게 자기효능감과 인지된 유용성을 거처 정보보호 활동으로 이어질 수 있는가에 대한 것이다. 282개의 표본 데이터를 이용하여 수행한 연구결과, 인터넷 사용자가 인지하는 정보프라이버시에 대한 염려는 자기효능감, 인지된 유용성, 그리고 정보보호 활동에 유의한 영향을 미치는 것으로 도출되었다. 또한 인지된 유용성과 정보보호활동은 자기효능감에 의해 유의한 영향을 받는 것으로 나타났다. 그러나 예상과는 달리, 인지된 유용성은 정보보호 활동에 통계적으로 유의한 영향력을 미치지 않았다. 본 연구의 결과는 온라인 기업과 인터넷 사용자들뿐만 아니라, 정보보호 교육을 제공하는 교육 관련자들에게 의미 있는 함의를 제공할 것이다.

• International Journal of Computer Science & Network Security
• /
• 제24권6호
• /
• pp.99-108
• /
• 2024

The Internet of Things (IoT) is a novel concept that allows a large number of objects to be connected to the Internet while also allowing them to be controlled remotely. The Internet of Things is extensive and has become an almost inseparable part of our daily lives. Users' personal data is frequently obtained by these linked gadgets and stored online. In recent years, the security of acquired data has become a major concern. As devices grow more linked, privacy and security concerns grow more pressing, and they must be addressed as soon as possible. IoT implementations and devices are particularly vulnerable to attacks that might adversely affect customer security and privacy, which might have an impact on their practical utility. The goal of this study is to bring attention to the security and privacy concerns that exist in IoT systems. To that purpose, the paper examines security challenges at each level of the IoT protocol stack, identifies underlying impediments and critical security requirements, and provides a rapid overview of available security solutions for securing IoT in a layered environment.

• International Journal of Contents
• /
• 제15권4호
• /
• pp.44-49
• /
• 2019

This study is designed to examine antecedents and consequences of mobile advertising avoidance. However, to date, research on mobile advertising avoidance has been scarce. Thus, this study makes significant contributions by addressing understudied areas in mobile advertising. Study results show that the perceived mobile advertising risk is positively related to mobile advertising avoidance. This study found that the perceived trust in mobile advertising is negatively linked to mobile advertising avoidance. The study results show that the perceived Internet users' data privacy concerns is positively linked to mobile advertising avoidance. Finally, study results suggest that mobile advertising avoidance is positively linked to intention to delete the ad.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제15권2호
• /
• pp.540-557
• /
• 2021

Privacy vulnerability of social networks is one of the major concerns for social science research and business analysis. Most existing studies which mainly focus on un-weighted network graph, have designed various privacy models similar to k-anonymity to prevent data disclosure of vertex attributes or relationships, but they may be suffered from serious problems of huge information loss and significant modification of key properties of the network structure. Furthermore, there still lacks further considerations of privacy protection for important sensitive edges in weighted social networks. To address this problem, this paper proposes a privacy preserving method to protect sensitive weighted edges. Firstly, the sensitive edges are differentiated from weighted edges according to the edge betweenness centrality, which evaluates the importance of entities in social network. Then, the perturbation operations are used to preserve the privacy of weighted social network by adding some pseudo-edges or modifying specific edge weights, so that the bottleneck problem of information flow can be well resolved in key area of the social network. Experimental results show that the proposed method can not only effectively preserve the sensitive edges with lower computation cost, but also maintain the stability of the network structures. Further, the capability of defending against malicious attacks to important sensitive edges has been greatly improved.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2013년도 춘계학술발표대회
• /
• pp.621-624
• /
• 2013

Mobile Cloud Computing is today's emerging technology. Customers enjoy the services and application from this combination of mobile technology and cloud computing. Beside all these benefits it also increases the concerns regarding privacy of users, while interacting with this new paradigm One of the services is Location based services, but to get their required services user has to give his/her current location to the LBS provider that is violation of location privacy of mobile client. Many approaches are in literature for preserve location privacy but some has computation restriction and some suffer from lack of privacy. In this paper we proposed a novel idea that not only efficient in its protocol but also completely preserves the user's privacy. The result shows that by sharing just service name and a large enough geographic area (e.g. a city) user gets required information from the server by doing little client side processing We perform experiments at client side by developing and testing an android based mobile client application to support our argument.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제4권3호
• /
• pp.411-427
• /
• 2010

Data sharing is an essential process for collaborative works particularly in the banking, finance and healthcare industries. These industries require many collaborative works with their internal and external parties such as branches, clients, and service providers. When data are shared among collaborators, security and privacy concerns becoming crucial issues and cannot be avoided. Privacy is an important issue that is frequently discussed during the development of collaborative systems. It is closely related with the security issues because each of them can affect the other. The tradeoff between privacy and security is an interesting topic that we are going to address in this paper. In view of the practical problems in the existing approaches, we propose a collaborative framework which can be used to facilitate concurrent operations, single point failure problem, and overcome constraints for two-party computation. Two secure computation protocols will be discussed to demonstrate our collaborative framework.

• International Journal of Computer Science & Network Security
• /
• 제22권4호
• /
• pp.340-350
• /
• 2022

The big data term refers to the great volume of data and complicated data structure with difficulties in collecting, storing, processing, and analyzing these data. Big data analytics refers to the operation of disclosing hidden patterns through big data. This information and data set cloud to be useful and provide advanced services. However, analyzing and processing this information could cause revealing and disclosing some sensitive and personal information when the information is contained in applications that are correlated to users such as location-based services, but concerns are diminished if the applications are correlated to general information such as scientific results. In this work, a survey has been done over security and privacy challenges and approaches in big data. The challenges included here are in each of the following areas: privacy, access control, encryption, and authentication in big data. Likewise, the approaches presented here are privacy-preserving approaches in big data, access control approaches in big data, encryption approaches in big data, and authentication approaches in big data.