• Journal of Korea Society of Digital Industry and Information Management
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• v.4 no.2
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• pp.57-66
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• 2008

There have been proposed various copyright protection protocols in network-based digital multimedia distribution framework. However, most of conventional copyright protection protocols are focused on the stability of copyright information embedding/extracting and the access control to data suitable for user's authority but overlooked the privacy of copyright owner and user in authentication process of copyright and access information. In this paper, we propose a solution that builds a privacy-preserving proof of copyright ownership of digital contents in conjunction with keyword search scheme. The appeal of our proposal is three-fold: (1) content providers maintain stable copyright ownership in the distribution of digital contents; (2) the proof process of digital contents ownership is very secure in the view of preserving privacy; (3) the proposed protocol is the copyright protection protocol added by indexing process but is balanced privacy and efficiency concerns for its practical use.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2768-2780
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• 2019

Ciphertext-policy attribute-based encryption (CP-ABE) is one of the practical technologies to share data over cloud since it can protect data confidentiality and support fine-grained access control on the encrypted data. However, most of the previous schemes only focus on data confidentiality without considering data receiver privacy preserving. Recently, Li et al.(in TIIS, 10(7), 2016.7) proposed a CP-ABE with hidden access policy and testing, where they declare their scheme achieves privacy preserving for the encryptor and decryptor, and also has high decryption efficiency. Unfortunately, in this paper, we show that their scheme fails to achieve hidden access policy at first. It means that any adversary can obtain access policy information by a simple decisional Diffie-Hellman test (DDH-test) attack. Then we give a method to overcome this shortcoming. Security and performance analyses show that the proposed scheme not only achieves the privacy protection for users, but also has higher efficiency than the original one.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.2
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• pp.171-179
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• 2008

RBAC(Role based Access Control) is effective way of managing user's access to information object in enterprise level and e-government system. The concept of RBAC is that the access right to object in a system is not directly assigned o users but assigned by being a member of a role which is defined in a organization. RBAC is utilized for controling access range of privacy but it does not support the personal legal right of control over information and right of limited access to the self. Nor it contains the way of observation of privacy flow that is guided in a legal level. In this paper, extended RBAC model for protecting privacy will be suggested and discussed. Two components of Data Right and Assigning Data Right are added to existed RBAC and the definition of each component is redefined in aspect of privacy preservation. Data Right in extended RBAC represents the access right to privacy data. This component provides the way of control over who can access which privacy and ensures limitation of access quantity of privacy. Based on this extended RBAC, implemented examples are presented and the evaluation is discussed by comparing existed RBAC with extended RBAC.

• ETRI Journal
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• v.30 no.6
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• pp.868-870
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• 2008

The rotation-based transformation (RBT) for privacy preserving data mining is vulnerable to the independent component analysis (ICA) attack. This paper introduces a modified multiple-rotation-based transformation technique for special mining applications, mitigating the ICA attack while maintaining the advantages of the RBT.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.1
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• pp.183-193
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• 2014

Korean personal data(information) protection law requires privacy policies post on every website. According to recent survey results, users' interests on these policies are low due to these policies' low readability and accessibility. This study proposes a layout that effectively conveys online privacy policy contents, and assesses its impact on information understandability, vividness, and recognition of users. Studies on privacy policies and layouts, media richness theory, social presence theory, and usability are used to develop the new layered approach. Using experiments, three major layouts are evaluated by randomly selected online users. Research results shows that information understandability, vividness, and recognition of privacy policies in the revised-layered approach are higher than those of in the text-only or table-based layouts. This study implies that employing visual guides like icons on privacy policy layouts may increase users' interest in those policies.

• 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.

• 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 the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.679-691
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• 2020

In the future, 5G technology will become the core infrastructure driving the 4th industrial era. For intelligent super-convergence service, it will be necessary to collect various personal information such as location data. If a person's high-precision location information is exposed by a malicious person, it can be a serious privacy risk. In the past, various approaches have been researched through encryption and obfuscation to protect location information privacy. In this paper, we proposed a new technique that enables statistical query and data analysis without exposing location information. The proposed method does not allow the original to be re-identified through polynomial-based transform processing. In addition, since the quality of the original data is not compromised, the usability of positioning big data can be maximized.

• Journal of KIISE:Databases
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• v.33 no.7
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• pp.741-753
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• 2006

As the total amount of traffic data in network has been growing at an alarming rate, many researches to mine traffic data with the purpose of getting useful information are currently being performed. However, network users' privacy can be compromised during the mining process. In this paper, we propose an efficient and practical privacy preserving sequential pattern mining method on network traffic data. In order to discover frequent sequential patterns without violating privacy, our method uses the N-repository server model and the retention replacement technique. In addition, our method accelerates the overall mining process by maintaining the meta tables so as to quickly determine whether candidate patterns have ever occurred. The various experiments with real network traffic data revealed tile efficiency of the proposed method.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.647-654
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• 2023

Deep Learning is a useful method for classifying and recognizing complex data such as images and text, and the accuracy of the deep learning method is the basis for making artificial intelligence-based services on the Internet useful. However, the vast amount of user da vita used for training in deep learning has led to privacy violation problems, and it is worried that companies that have collected personal and sensitive data of users, such as photographs and voices, own the data indefinitely. Users cannot delete their data and cannot limit the purpose of use. For example, data owners such as medical institutions that want to apply deep learning technology to patients' medical records cannot share patient data because of privacy and confidentiality issues, making it difficult to benefit from deep learning technology. In this paper, we have designed a privacy preservation technique-applied deep learning technique that allows multiple workers to use a neural network model jointly, without sharing input datasets, in multi-party system. We proposed a method that can selectively share small subsets using an optimization algorithm based on modified stochastic gradient descent, confirming that it could facilitate training with increased learning accuracy while protecting private information.