• 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 Information Processing Systems
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• v.7 no.3
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• pp.549-560
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• 2011

Vote validity proof and verification is an efficiency bottleneck and privacy drawback in homomorphic e-voting. The existing vote validity proof technique is inefficient and only achieves honest-verifier zero knowledge. In this paper, an efficient proof and verification technique is proposed to guarantee vote validity in homomorphic e-voting. The new proof technique is mainly based on hash function operations that only need a very small number of costly public key cryptographic operations. It can handle untrusted verifiers and achieve stronger zero knowledge privacy. As a result, the efficiency and privacy of homomorphic e-voting applications will be significantly improved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5244-5259
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• 2019

With the continuous development of LBS (Location Based Service) applications, privacy protection has become an urgent problem to be solved. Differential privacy technology is based on strict mathematical theory that provides strong privacy guarantees where it supposes that the attacker has the worst-case background knowledge and that knowledge has been applied to different research directions such as data query, release, and mining. The difficulty of this research is how to ensure data availability while protecting privacy. Spatial multidimensional data are usually released by partitioning the domain into disjointed subsets, then generating a hierarchical index. The traditional data-dependent partition methods need to allocate a part of the privacy budgets for the partitioning process and split the budget among all the steps, which is inefficient. To address such issues, a novel two-step partition algorithm is proposed. First, we partition the original dataset into fixed grids, inject noise and synthesize a dataset according to the noisy count. Second, we perform IH-Tree (Improved H-Tree) partition on the synthetic dataset and use the resulting partition keys to split the original dataset. The algorithm can save the privacy budget allocated to the partitioning process and obtain a more accurate release. The algorithm has been tested on three real-world datasets and compares the accuracy with the state-of-the-art algorithms. The experimental results show that the relative errors of the range query are considerably reduced, especially on the large scale dataset.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.9
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• pp.1653-1667
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• 2011

Privacy is one of the most important and difficult research issues in ubiquitous computing. It is qualitative rather than quantitative. Privacy preserving mainly relies on policy based rules of the system, and users cannot adjust their privacy disclosure rules dynamically based on their wishes. To make users understand and control their privacy measurement, we present a scheme to quantize the personal privacy. We aim to configure the person's privacy based on the numerical privacy level which can be dynamically adjusted. Instead of using the traditional simple rule engine, we implement this scheme in a complex way. In addition, we design the scenario to explain the implementation of our scheme. To the best of our knowledge, we are the first to assess personal privacy numerically to achieve precision privacy computing. The privacy measurement and disclosure model will be refined in the future work.

• 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.

• International Journal of Knowledge Content Development & Technology
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• v.4 no.1
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• pp.53-78
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• 2014

As instances of private information leak increase, taking steps to protect such information becomes a necessity. In this study of public library patrons, we strove for a comprehensive understanding of library usage records to suggest viable solutions for private information safety in public libraries. To this end, we investigated the patrons' understanding of library usage records and determined the relationship between different user characteristics and privacy knowledge or leaks. The results show that a high number of patrons perceived these records as their own private information, but that there was no necessity for legal procedures or consent for the use of these records. Also, even though the understanding of these usage records showed that there was a relationship between the frequency of library visits and leaks of personal information, the correlation was not particularly strong.

• Knowledge Management Research
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• v.13 no.4
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• pp.83-100
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• 2012

Given the prevalence of mobile social network services (SNS) such as Facebook and Kakaotalk, it has become important to understand user's continuance behavior in a mobile SNS environment. Although trust and privacy concerns play a key role in SNS users' decision-making processes, most studies on SNS have shed little light on the effects of trust and privacy concerns on SNS continuance intention. In this regard, this paper developed an integrated model to deeply understand the key antecedents of user's continuance intention to use mobile SNS by incorporating trust and privacy concerns into extended expectation-confirmation model. The proposed research model was tested by using survey data collected from 170 users who have experience with Kakaotalk. The findings of this study found that the proposed theoretical framework provides a statistically significant explanation of the variance in continuance intention of mobile SNS. The analysis results indicate that trust serves as the salient antecedent of continuance intention to use mobile SNS. However, it was found that privacy concerns negatively influence trust, whereas it is not significantly related to continuance intention of mobile SNS. The theoretical and practical implications of the findings were described.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.6
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• pp.1285-1303
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• 2019

In the current credit scoring system, the credit bureau gathers credit information from financial institutions and calculates a credit score based on it. However, because all sensitive credit information is stored in one central authority, there are possibilities of privacy violations and successful external attacks can breach large amounts of personal information. To handle this problem, we propose privacy-preserving credit scoring in which a user gathers credit information from financial institutions, calculates a credit score and proves that the score is calculated correctly using a zero-knowledge proof and a blockchain. In addition, we propose a zero-knowledge proof scheme that can efficiently prove committed inputs to check whether the inputs of a zero-knowledge proof are actually provided by financial institutions with a blockchain. This scheme provides perfect zero-knowledge unlike Agrawal et al.'s scheme, short CRSs and proofs, and fast proof and verification. We confirmed that the proposed credit scoring can be used in the real world by implementing it and experimenting with a credit score algorithm which is similar to that of the real world.

• Journal of the Korean Home Economics Association
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• v.37 no.10
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• pp.55-66
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• 1999

The purpose of this study is to propose consumer policy related to the protection of personal information on the basis of regulations and laws in the developed countries. From this study, implications for the protection consumer privacy are discussed as follows. First, Consumer education is needed to enhance consumers'knowledge on their privacy right and this should be done not only by private consumer organization but also by businesses. Second, Businesses should realize ethical responsibilities of consumers'privacy right when they use personal information by databasemarketing. Finally, Government should establish a privacy law concerning both public and private sectors.

• Asia pacific journal of information systems
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• v.30 no.4
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• pp.919-945
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• 2020

Social media platforms such as Instagram and Facebook lead to potential security risks, which consequently raise public concerns about privacy. However, most people rarely make active efforts to protect their personal data, even though they have shown increasing concerns about privacy. Therefore, this study examines the factors that determine social media users' behavior of using privacy settings and testifies the existence of privacy paradox in such a context. In addition, it investigates the mediating effects of implementation intentions on the relationship between intentions and behaviors. In the study, we collected data through questionnaires, and the respondents were undergraduate and graduate students in South Korea. After a pilot test (n = 92) and a set of face-to-face interviews, 266 usable responses were retrieved for data analysis finally. The results confirmed the existence of the privacy paradox regarding the use of social media privacy settings. And the implication intention did positively mediate the relationship between intention and behavior in the context of social media privacy settings. To the best of our knowledge, our study is the first in the information privacy literature to introduce the notion of implementation intention which is a much more powerful explanation and prediction of actual behavior than the (behavioral) intention.