• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.8
• /
• pp.3328-3349
• /
• 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
• /
• v.42 no.3
• /
• pp.387-406
• /
• 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.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.3
• /
• pp.181-189
• /
• 2024

The purpose of this study is to investigate the role of interaction privacy management behavior (Use of IPCs), which has received relatively little attention. To this end, this study proposes an integrated model that theorizes the relationship between the main variables of the privacy calculation model and interaction privacy management behavior. Empirical analysis of this model shows that the use of IPCs lowers risks, increases benefits, and in turn promotes increased self-disclosure. These results have implications for expanding the theoretical logic of the privacy calculation model because users' self-disclosure includes not only the limited exposure proposed in the model but also unrestricted exposure through the use of IPC.

• The Journal of Information Systems
• /
• v.25 no.4
• /
• pp.1-21
• /
• 2016

Purpose The study analyzed the factors affecting continuance use and self-disclosure in the SNS(social networking service) context based on a dual-factor model. As SNS users have concerned privacy for a long time, privacy concern affects continuous use and self-disclosure. In details, concern over privacy may have a stronger effect on self-disclosure than on continuance use as users' personal information can be more exposed during posting their dailies and photos. Design/Methodology/Approach SNS benefits, trust in SNS providers, and social influence are served as the key enablers and privacy concern as the inhibitor. Moreover, the relative impacts of SNS benefits and privacy concern on continuance use and self-disclosure were analysed in this study. From the data of 327 Facebook users, the researchers tested proposed theoretical model by using PLS. Findings Users' continuance intention and self-disclosure behavior are differently affected by different antecedents. Trust in SNS provider had a significant effect on self-disclosure intention, while it has no significant effect on continuance intention. Concern over privacy was negatively related to self-disclosure intention, while it was positively associated with continuance intention.

• Convergence Security Journal
• /
• v.21 no.3
• /
• pp.49-56
• /
• 2021

Recently, artificial intelligence is regarded as an essential technology in our society. In particular, the invasion of privacy in artificial intelligence has become a serious problem in modern society. Split learning, proposed at MIT in 2019 for privacy protection, is a type of federated learning technique that does not share any raw data. In this study, we studied a safe and accurate segmentation learning model using known differential privacy to safely manage data. In addition, we trained SVHN and GTSRB on a split learning model to which 15 different types of differential privacy are applied, and checked whether the learning is stable. By conducting a learning data extraction attack, a differential privacy budget that prevents attacks is quantitatively derived through MSE.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.20 no.4
• /
• pp.135-144
• /
• 2010

Privacy protection can only be achieved by enforcing privacy policies within an enterprise's on and offline data processing systems. There are P-RBAC model and purpose based model and obligations model among privacy policy models. But only these models each can not dynamically deal with the rapidly changing business environment. Even though users are in the same role, on occasion, secure system has to opt for a figure among them who is smart, capable and supremely confident and to give him/her a special mission during a given period and to strengthen privacy protection by permitting to present fluently access control conditions. For this, we propose Integrated Privacy Protection Model based on RBAC. Our model includes purpose model and P-RBAC and obligation model. And lastly, we define high level policy language model based XML to be independent of platforms and applications.

• Journal of Information Processing Systems
• /
• v.15 no.5
• /
• pp.1036-1054
• /
• 2019

Recently, concomitant with a surge in numbers of Internet of Things (IoT) devices with various sensors, mobile crowdsensing (MCS) has provided a new business model for IoT. For example, a person can share road traffic pictures taken with their smartphone via a cloud computing system and the MCS data can provide benefits to other consumers. In this service model, to encourage people to actively engage in sensing activities and to voluntarily share their sensing data, providing appropriate incentives is very important. However, the sensing data from personal devices can be sensitive to privacy, and thus the privacy issue can suppress data sharing. Therefore, the development of an appropriate privacy protection system is essential for successful MCS. In this study, we address this problem due to the conflicting objectives of privacy preservation and incentive payment. We propose a privacy-preserving mechanism that protects identity and location privacy of sensing users through an on-demand incentive payment and group signatures methods. Subsequently, we apply the proposed mechanism to one example of MCS-an intelligent parking system-and demonstrate the feasibility and efficiency of our mechanism through emulation.

• Journal of Information Technology Applications and Management
• /
• v.15 no.4
• /
• pp.37-59
• /
• 2008

This study focuses on the antecedents to the privacy concerns and their influence on trust and online transaction intention. Based on previous exploratory works and the literature review of privacy concerns, four antecedents are identified-Internet literacy, social awareness, perceived vulnerability, and perceived ability to information control. Incorporating these antecedents, privacy concerns, trust and online transaction intention, a conceptual model is developed and seven research hypotheses are proposed for empirical testing. The proposed model is examined through structural equation analysis. The results show that Internet literacy, social awareness, and perceived vulnerability have statistically significant effect on the privacy concerns of users and the privacy concerns has a positive influence on the trust. Finally, the trust has a positive effect on the online transaction intention. Implications of these findings are discussed for both researchers and practitioners and future research issues are raised as well.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.10
• /
• pp.2708-2730
• /
• 2012

The growing concern for the protection of personal information has made it critical to implement effective technologies for privacy and data management. By observing the limitations of existing approaches, we found that there is an urgent need for a flexible, privacy-aware system that is able to meet the privacy preservation needs at both the role levels and the personal levels. We proposed a conceptual system that considered these two requirements: a graph-based, access control model to safeguard patient privacy. We present a case study of the healthcare field in this paper. While our model was tested in the field of healthcare, it is generic and can be adapted to use in other fields. The proof-of-concept demos were also provided with the aim of valuating the efficacy of our system. In the end, based on the hospital scenarios, we present the experimental results to demonstrate the performance of our system, and we also compared those results to existing privacy-aware systems. As a result, we ensured a high quality of medical care service by preserving patient privacy.

• ETRI Journal
• /
• v.40 no.6
• /
• pp.813-823
• /
• 2018

In existing mobile computing environments, users need to choose between their privacy and the services that they can receive from an application. However, existing mobile platforms do not allow users to perform such trade-offs in a fine-grained manner. In this study, we investigate whether users can effectively make utility-privacy trade-offs when they are provided with a multilevel privacy control method that allows them to recognize the different quality of service that they will receive from an application by limiting the disclosure of their private information in multiple levels. We designed a research model to observe users' utility-privacy trade-offs in accordance with the privacy control methods and other factors such as the trustworthiness of an application, quality level of private information, and users' privacy preferences. We conducted a user survey with 516 participants and found that, compared with the existing binary privacy controls, both the service utility and the privacy protection levels were significantly increased when the users used the multilevel privacy control method.