• Information Systems Review
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• v.18 no.3
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• pp.185-207
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• 2016

This study investigated the relationship between privacy policy awareness and willingness to provide personal information. Online privacy policies published on the Internet aim to build the trust of consumers and reduce their concerns about the provision of providing personal information. This study uses FIP(FIP; Fair Information Practices) principles to measure awareness of privacy policy. The result of the survey indicates significant relationships among awareness of privacy policy of e-commerce websites, privacy trust, and privacy risk. Privacy policy aims to improve transparency of collection and use of personal information. A high level of privacy trust is related to a high level of willingness to provide personal information on an e-commerce website. A low level of privacy risk is related to a high level of willingness to provide personal information on an e-commerce website. This study found that disposition to trust moderates the relationship between privacy policy awareness and privacy trust. This study contributes to further research on the relationships among privacy policy awareness, privacy trust, and privacy risk. The result of this study can be used by companies that aim to build privacy trust and reduce privacy risk.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.47-54
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• 2022

Recently, with the development of deep learning technology, research on recognizing human behavior is in progress. In this paper, a study was conducted to recognize risky behaviors that may occur in a single-person household environment using deep learning technology. Due to the nature of single-person households, personal privacy protection is necessary. In this paper, we recognize human dangerous behavior in privacy protection video with Gaussian blur filters for privacy protection of individuals. The dangerous behavior recognition method uses the YOLOv5 model to detect and preprocess human object from video, and then uses it as an input value for the behavior recognition model to recognize dangerous behavior. The experiments used ResNet3D, I3D, and SlowFast models, and the experimental results show that the SlowFast model achieved the highest accuracy of 95.7% in privacy-protected video. Through this, it is possible to recognize human dangerous behavior in a single-person household environment while protecting individual privacy.

• Journal of Digital Convergence
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• v.14 no.12
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• pp.1-12
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• 2016

Today, with the popularity of smart phones and the proliferation of SNS, anyone is exposed to the risk of personal information leakage. Unlike the prior studies of privacy, this research aims to identify the privacy factors affecting the provision intention of individual information from the SNS Users. This study also analyses how the perceived privacy risks and corporate trust affect the provision intention of individual information. The analysis results of empirical data show that despite experiencing the privacy leakage such as direct hacking and being aware of the risk, people are providing firms with personal information. The most influential variables to perceived privacy risk are information privacy policy, information privacy concern, previous privacy experience and information privacy awareness in the decreasing order of importance. Those to the corporate trust are information privacy policy, information privacy awareness, previous privacy concern and information privacy experience. Besides, the corporate trust and the perceived privacy risk also affect the provision intention of personal information. Finally, this study proposes the implications for personal information privacy.

• Management & Information Systems Review
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• v.36 no.1
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• pp.81-94
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• 2017

In the age of information overload such as Internet of Things(IoT), big data, and cloud computing, Data and informations are collected to processed regardless of the individual's will. The purpose of this paper presents a model related to personal information overlord, information privacy risk, information privacy concern (collection, control, awareness) and personal information privacy protective response. The results of this study is summarized as follows. First, personal information overload significantly affects information privacy risk. Second, personal information overload significantly affects information privacy concern(collection, control, awareness) Third, information privacy risk significantly affects collection and awareness among information privacy concern, but control does not significantly affects. This results shows that users are cognitively aware the information risk through collection and awareness of information. Users can not control information by self, control of information does not affects. Last, information privacy concern(collection and awareness significantly affect information privacy protective response, but information privacy concern (control) does not affect. Personal information users are concerned about information infringement due to excessive personal information, ability to protect private information became strong.

• Informatization Policy
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• v.20 no.3
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• pp.63-85
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• 2013

Drawing on Protection Motivation Theory(PMT), this study attempts to clarify antecedents that influence the intention to protect individuals' privacy on the Internet. Protection motivation forms through individuals' cognitive appeal involving threat and efficacy. Then protection motivation causes privacy behavioral change. Protection motivation factors are established privacy trust and privacy risk, which are related to privacy attitude and belief. This proposed model is empirically analyzed by utilizing structural equation analysis(SEM). According to the result of the empirical analysis, it is founded that almost paths have statistically significant explanatory power except path from efficacy to privacy risk and path from privacy trust to privacy behavioral intention. This study shows powerful evidence of antecedent factors based on protection motivation of individuals' privacy behavioral intention in online environment.

• Journal of Digital Convergence
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• v.11 no.1
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• pp.107-119
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• 2013

The purpose of this study is to find an answer why internet users are unconcern about their privacy information. We found that perceived privacy risk and website usability have a significant effect on privacy unconcern. That is, individuals who have experiences privacy incidents are more likely to be unconcern about their privacy information. Accordingly, organizations who supply services on the web have to pay more attention to these individuals to increase a privacy concern. Implications and Conclusions are discussed.

• 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.

• Journal of Korean Library and Information Science Society
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• v.43 no.3
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• pp.353-384
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• 2012

This study was conducted on the observation that the filter bubble and privacy violation problems are related to the personalized services provided by libraries. This study discussed whether there is the possibility for invasion of privacy when libraries provide services utilizing state-of-the-art technology, such as location-based services, context aware services, RFID-based services, Cloud Services, and book recommendation services. In addition, this study discussed the following three aspects: whether or not users give up their right to privacy when they provide personal information for online services, whether or not there are discussions about users' privacy in domestic libraries, and what kind of risks the filter bubble problem can cause library users and what are possible solutions. This study represents early-stage research on library privacy in Korea, and can be used as basic data for privacy research.

• 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 Bigdata
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• v.5 no.2
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• pp.201-213
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• 2020

Despite empirical research that the response to human risk is significantly influenced affective factors, the role of affective factors has been unexplored in information privacy research. This study aims to explore the privacy behaviors of location-based service (LBS) users from an affective point of view. Specifically, the study explored the relationship between three types of privacy threats (collection, hacking, secondary use), two affects (worry, anger), and a coping behavior (continuous use intentions). The structured survey was conducted with 552 users. In order to analyze the effect of the combination of perception of particular privacy threats and particular affects on the intention of continuous use, association rules, one of the data mining techniques, was employed. As a result, there was a difference in the intention to use according to the combination of the perception of risk and affect responses, and the most significant influence on the intention is when the second use of personal information was combined with anger. This study has significant theoretical contribution in that it includes affective factors in the research of information privacy users, complementing the biases of existing cognition-oriented approaches and providing a comprehensive understanding of privacy response behavior.