• Journal of the Korean Institute of Landscape Architecture
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• v.23 no.1
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• pp.95-109
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• 1995

All human spatial behavior and psychological stress are affected by the 'Privacy'of each space. This Paper deals with the theoretical review of 'privacy'concept and establishment of 'Privacy Model' that can be a useful design tool. 'Privacy Index(Pl)' model of 10 point scale, which is based on 'Hierarchic system of Privacy' in urban spaces by Chermeyeff and Alexander(1963), was established as a hypothetical model in this study. And'Activity Suitability', based on each hierarchy of primacy level, was investigated at each site to construct the validity of 'Privacy Model'. Total 67 sites were investigated by on.-site questionnaire in 3 types of outdoor spaces, (Park), (Campus), and (Garden) respectively. The major results are as follows; 1. The P7rivacy level of earth spaces, distributed from to in and . and (Groun Private> spaces are dominant In , spaces are dondnant 2, Privacy level, based on , showed higher privacy level than that of . This means the criteria of each privacy level should be modified for more specific space. The . could be derived from the (Activity Suitability) of each space. 3.The cognition of privacy level. by user group, showed no significant difference in dach group by sex, age, education, and job, respectively.

• Journal of Internet Computing and Services
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• v.7 no.1
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• pp.1-16
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• 2006

The vulnerability of privacy in the Identity Management System (IMS) is the most pressing concern of ordinary users. Uncertainty about privacy keeps many users away from utilization of IMS. Therefore, this paper proposes an access-control oriented privacy model for IMS. The proposed model protects privacy using access control techniques with privacy policies in a single circle of trust. We address characteristics of the components of for the proposed model and describe access control procedures. After that, we show the architecture of privacy enforcement and XML-based schema for privacy policies.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.8C
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• pp.1076-1085
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• 2004

Privacy enforcement has been one of the most important problems in IT area. Privacy protection can be achieved by enforcing privacy policies within an organization's data processing systems. Traditional security models are more or less inappropriate for enforcing basic privacy requirements, such as privacy binding. This paper proposes an extended role-based access control (RBAC) model for enforcing privacy policies within an organization. For providing privacy protection and context based access control, this model combines RBAC, Domain-Type Enforcement, and privacy policies Privacy policies are to assign privacy levels to user roles according to their tasks and to assign data privacy levels to data according to consented consumer privacy preferences recorded as data usage policies. For application of this model, small hospital model is considered.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.3C
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• pp.167-175
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• 2005

Privacy enforcement has been one of the most important problems in IT area. Privacy protection can be achieved by enforcing privacy policies within an organization's online and offline data processing systems. Traditional security models are more or less inappropriate for enforcing basic privacy requirements, such as purpose binding. This paper proposes a new approach in which a privacy control model is derived from integration of an existing security model. To this, we use an extended role-based access control model for existing security mechanism, in which this model provides context-based access control by combining RBAC and domain-type enforcement. For implementation of privacy control model we use GRBAC(Generalized Role-Based Access Control), which is expressive enough to deal with privacy preference. And small hospital model is considered for application of this model.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.589-598
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• 2019

The amount of digital data a is explosively growing, and these data have large potential values. Countries and companies are creating various added values from vast amounts of data, and are making a lot of investments in data analysis techniques. The privacy problem that occurs in data analysis is a major factor that hinders data utilization. Recently, as privacy violation attacks on neural network models have been proposed. researches on artificial neural network technology that preserves privacy is required. Therefore, various privacy preserving artificial neural network technologies have been studied in the field of differential privacy that ensures strict privacy. However, there are problems that the balance between the accuracy of the neural network model and the privacy budget is not appropriate. In this paper, we study differential privacy techniques that preserve the performance of a model within a given privacy budget and is resistant to model inversion attacks. Also, we analyze the resistance of model inversion attack according to privacy preservation strength.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.81-92
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• 2023

A privacy model is a technique that quantitatively restricts the possibility and degree of privacy breaches through privacy attacks. Representative models include k-anonymity, l-diversity, t-closeness, and differential privacy. While many privacy models have been studied, research on selecting the most suitable model for a given dataset has been relatively limited. In this study, we develop a system for recommending the suitable privacy model to prevent privacy breaches. To achieve this, we analyze the data features that need to be considered when selecting a model, such as data type, distribution, frequency, and range. Based on privacy model background knowledge that includes information about the relationships between data features and models, we recommend the most appropriate model. Finally, we validate the feasibility and usefulness by implementing a recommendation prototype system.

• The Journal of Information Systems
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• v.25 no.3
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• pp.197-215
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• 2016

Purpose This study aims to investigate the factors that influence decision making in relation to providing personal information on the internet with respect to the integration of the privacy calculus theory and protection motivation theory based on the dual-calculus model proposed by Li(2012). Design/methodology/approach The privacy calculus theory and protection motivation theory have been applied to explain privacy behavior to a certain degree but few studies have been conducted to explain privacy behavior based on the integration of these two theories. Although Li(2012) proposed the dual-calculus model, he only proposed its framework and did not carry out an empirical study. Therefore, this study proposes a research model that integrates these two theories and examines the relationship between the two theories through an empirical study. Findings According to the results of empirical analysis, it was found that all relations have statistically significant explanatory power except the relation between coping appraisal and privacy risk in the risk calculus process. Thus, the results verify that external threat played a decisive role in increasing the risk level of a consumer's privacy. It can be discussed the ways to enhance the privacy behavior of consumer on the internet through these findings.

• The Journal of Information Systems
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• v.26 no.2
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• pp.43-62
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• 2017

Purpose This study provided empirical support for the model that explain the formation of privacy concerns in the perspective of Information Boundary Theory. This study investigated an integrated model suggesting that privacy concerns are formed by the individual's disposition to value privacy, privacy awareness, awareness of privacy policy, and government legislation. The Information Boundary Theory suggests that the boundaries of information space dependends on the individual's personal characteristics and environmental factors of e-commerce. When receiving a request for personal information from e-commerce websites, an individual assesses the risk depending on the risk-control assessment, the perception of intrusion give rise to privacy concerns. Design/methodology/approach This study empirically tested the hypotheses with the data collected in a survey that included the items measuring the constructs in the model. The survey was aimed at university students. and a causal modeling statistical technique(PLS) is used for data analysis in this research. Findings The results of the survey indicated significant relationships among environmental factors of e-commerce websites, individual's personal privacy characteristics and privacy concerns. Both individual's awareness of institutional privacy assurance on e-commerce and the privacy characteristics affect the risk-control assessment towards information disclosure, which becomes an essential components of privacy concerns.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3328-3349
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• 2020

With rapid development of ubiquitous computing and location-based services (LBSs), human trajectory data and associated activities are increasingly easily recorded. Inappropriately publishing trajectory data may leak users' privacy. Therefore, we study publishing trajectory data while preserving privacy, denoted privacy-preserving activity trajectories publishing (PPATP). We propose S-PPATP to solve this problem. S-PPATP comprises three steps: modeling, algorithm design and algorithm adjustment. During modeling, two user models describe users' behaviors: one based on a Markov chain and the other based on the hidden Markov model. We assume a potential adversary who intends to infer users' privacy, defined as a set of sensitive information. An adversary model is then proposed to define the adversary's background knowledge and inference method. Additionally, privacy requirements and a data quality metric are defined for assessment. During algorithm design, we propose two publishing algorithms corresponding to the user models and prove that both algorithms satisfy the privacy requirement. Then, we perform a comparative analysis on utility, efficiency and speedup techniques. Finally, we evaluate our algorithms through experiments on several datasets. The experiment results verify that our proposed algorithms preserve users' privay. We also test utility and discuss the privacy-utility tradeoff that real-world data publishers may face.

• Journal of Korean Society for Quality Management
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• v.42 no.3
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• pp.387-406
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• 2014

Purpose: The widespread use of social network services (SNS) has caused users concern about the disclosure of their privacy or personal information. The purpose of this study is to analyze the factors of privacy concern and self presentation that affect the user resistance in the use of social network service. Methods: This study verifies the factors that affecting the user resistance in SNS. The research model suggested in this study is tested via a survey of 260 SNS users. SPSS and Smart PLS had been used to test the suggested hypotheses. Results: This study shows that privacy experience, privacy awareness, self esteem, and social desirability significantly influence perceived risk and that privacy awareness, self esteem, self efficacy, and perceived risk significantly influence perceived trust. It also verifies that perceived risk and perceived trust positively affect user resistance. Conclusion: This paper suggests that high awareness on privacy of SNS user encourages the SNS companies to consider the privacy protection mechanism for eliminating various factors that affecting the risk. This study also shows that the privacy calculus model applies to understanding the mechanism on resistance of SNS user.