• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3419-3437
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• 2022

Anonymization technology is an important technology for privacy protection in the process of data release. Usually, before publishing data, the data publisher needs to use anonymization technology to anonymize the original data, and then publish the anonymized data. However, for data publishers who do not have or have less anonymized technical knowledge background, how to configure appropriate parameters for data with different characteristics has become a more difficult problem. In response to this problem, this paper adds a historical configuration scheme resource pool on the basis of the traditional anonymization process, and configuration parameters can be automatically recommended through the historical configuration scheme resource pool. On this basis, a privacy model hybrid recommendation algorithm for user satisfaction is formed. The algorithm includes a forward recommendation process and a reverse recommendation process, which can respectively perform data anonymization processing for users with different anonymization technical knowledge backgrounds. The privacy model hybrid recommendation algorithm for user satisfaction described in this paper is suitable for a wider population, providing a simpler, more efficient and automated solution for data anonymization, reducing data processing time and improving the quality of anonymized data, which enhances data protection capabilities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4463-4483
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• 2019

Privacy-preserving collaborative filtering schemes take privacy concerns into its primary consideration without neglecting the prediction accuracy. Different schemes are proposed that are built upon different data partitioning scenarios such as a central server, two-, multi-party or peer-to-peer network. These data partitioning scenarios have been investigated in terms of claimed privacy promises, recently. However, to the best of our knowledge, any peer-to-peer privacy-preserving scheme lacks such study that scrutinizes privacy promises. In this paper, we apply three different attack techniques by utilizing auxiliary information to derive private ratings of peers and conduct experiments by varying privacy protection parameters to evaluate to what extent peers' data can be reconstructed.

• Knowledge Management Research
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• v.16 no.3
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• pp.23-40
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• 2015

Despite much interest and investment in the Internet of Things (IoT) which expand the Internet to a ubiquitous network including objects in the physical world, there is growing concerns of privacy protections. Because the risk of privacy invasion is higher in IoT environments than ever before, privacy need to be a key issue in the diffusion of IoT. Considering that the privacy concern is a critical barrier for user to adopt information technologies, it is important to investigate users' privacy concerns related to IoT applications. From the triad perspective (i.e., risk on technology, risk on service provider, and trust on legislation), this study aims to examine users' privacy concerns in the context of activity trackers.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.2
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• pp.501-522
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• 2016

In the digital era, information is a source of value creation. However, the growing importance of knowledge and information also increases risks and threats. When information is leaked, full recovery is difficult, and additional spreading of risk is high because it is easy to accomplish. Especially personal information is the main target due to its availability. Although individuals normally have to consent to the use of their personal information, they often do not know the use of their information. In such a difficult situation, one must exercise self-determination and privacy. Therefore, the goal of this study is to development a privacy literacy level measurement model for the proper exercise of the right to informational self-determination. It will be presented with the concept of privacy literacy index in order to determine the level of knowledge and understanding and practical application skills for individual. Through the index, we going to enhance the selection ability of information subject, and to promote the judgement and the determination capability for the protection and utilization of personal information.

• Asia pacific journal of information systems
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• v.21 no.3
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• pp.1-18
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• 2011

Blog have become very popular with Internet users as one of the latest forms of online communication as well as knowledge sharing tools. However, blogs generate growing concerns regarding information privacy issues. This study, based on social exchange theory, presents results about bloggers' information sharing behavior. The 157 surveys are collected from a large university in the eastern U.S. The survey results indicate that trust which has four second order factors: economy based trust, trust in reciprocity, trust in other bloggers and trust in social interaction positively affects bloggers' information sharing behavior. However, online information privacy concerns have a negative impact on the relationship between trust and bloggers' information sharing behavior.

• Knowledge Management Research
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• v.18 no.3
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• pp.63-80
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• 2017

Big Data environment is established by accumulating vast amounts of data as users continuously share and provide personal information in online environment. Accordingly, the more data is accumulated in online environment, the more data is accessible easily by third parties without users' permissions compared to the past. By utilizing strategies based on data-driven, firms recently make it possible to predict customers' preferences and consuming propensity relatively exactly. This Big Data environment, on the other hand, establishes 'Dataveillance' which means anybody can watch or control users' behaviors by using data itself which is stored online. Main objective of this study is to identify the relationship between Dataveillance and users' online privacy protection behaviors. To achieve it, we first investigate perceived online service efficiency; loss of control on privacy; offline surveillance; necessity of regulation influences on users' perceived threats which is generated by Dataveillance.

• 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 East-West Nursing Research
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• v.29 no.1
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• pp.24-32
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• 2023

Purpose: This study aims to examine level of perception and performance of privacy protection behavior of anesthesia and operating room (OR) nurses for patients who underwent general anesthesia surgery. Methods: Data collection was conducted from August 2020 to January 2021 for a total of 101 participants, consisting of 49 patients and 52 nurses. Independent t-test and Pearson's correlation were conducted using SPSS 21. Results: Anesthesia and OR nurses showed the highest score in patient privacy, followed by patient information management, body privacy, and the lowest score in communication. There was a significant difference between the patient information and the communication. Conclusion: Anesthesia and OR nurses had the highest level of perception and performance of patient privacy protection behavior for body privacy, and the lowest for communication. In addition, there was a significant difference in patient information management and communication. In order to protect the privacy of patients undergoing general anesthesia surgery, efforts are needed to learn standardized nursing knowledge, attitudes, and practice.

• Knowledge Management Research
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• v.24 no.1
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• pp.51-72
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• 2023

Digital literacy is vital knowledge and ability of an individual in the information society. As the level of digital literacy increases, the interest in privacy protection increases. This change may hinder the use of digital technologies and services. This research examines (1) the mediating effect of online social participation and information veracity on smart device literacy and privacy concerns, and (2) the moderating effect of privacy literacy. Using Korean media panel survey data reported in 2020 and in 2021, this study analyzes the responses of 7,737 people who use smart devices and participate in online activities. SPSS and PROCESS Macro are used to test the research model and hypotheses. In the analysis of 2020 and 2021 survey, this research shows that smart device literacy has major effects on privacy concerns; confirms that the mediating effect of online social participation; moderated meditating effect of privacy literacy. Although information veracity is not significant in 2020, mediating and moderated mediating effects are found in 2021.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5654-5668
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• 2018

Driven by security and real-time demands of Internet of Things (IoT), the timing of fog computing and edge computing have gradually come into place. Gateways bear more nearby computing, storage, analysis and as an intelligent broker of the whole computing lifecycle in between local devices and the remote cloud. In fog computing, the edge broker requires X-aware capabilities that combines software programmability, stream processing, hardware optimization and various connectivity to deal with such as security, data abstraction, network latency, service classification and workload allocation strategy. The prosperous of Field Programmable Gate Array (FPGA) pushes the possibility of gateway capabilities further landed. In this paper, we propose a software-defined gateway (SDG) scheme for fog computing paradigm termed as Fog Computing Zero-Knowledge Gateway that strengthens data protection and resilience merits designed for industrial internet of things or highly privacy concerned hybrid cloud scenarios. It is a proxy for fog nodes and able to integrate with existing commodity gateways. The contribution is that it converts Privacy-Enhancing Technologies rules into provable statements without knowing original sensitive data and guarantees privacy rules applied to the sensitive data before being propagated while preventing potential leakage threats. Some logical functions can be offloaded to any programmable micro-controller embedded to achieve higher computing efficiency.