• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.1
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• pp.33-43
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• 2013

The research on data mining that can manage a large amount of information efficiently has grown with the drastic increment of information. Privacy-preserving data mining can protect the privacy of data owners. There are several privacy-preserving association rule, clustering and classification protocols. A privacy-preserving association rule protocol is used to find association rules among data, which is often used for marketing. In this paper, we propose a privacy-preserving k-bits inner product protocol based on Shamir's secret sharing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.3
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• pp.558-575
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• 2013

The purpose of a data leakage prevention system is to protect corporate information assets. The system monitors the packet exchanges between internal systems and the Internet, filters packets according to the data security policy defined by each company, or discretionarily deletes important data included in packets in order to prevent leakage of corporate information. However, the problem arises that the system may monitor employees' personal information, thus allowing their privacy to be violated. Therefore, it is necessary to find not only a solution for detecting leakage of significant information, but also a way to minimize the leakage of internal users' personal information. In this paper, we propose two models for representing the level of personal information disclosure during data leakage detection. One model measures only the disclosure frequencies of keywords that are defined as personal data. These frequencies are used to indicate the privacy violation level. The other model represents the context of privacy violation using a private data matrix. Each row of the matrix represents the disclosure counts for personal data keywords in a given time period, and each column represents the disclosure count of a certain keyword during the entire observation interval. Using the suggested matrix model, we can represent an abstracted context of the privacy violation situation. Experiments on the privacy violation situation to demonstrate the usability of the suggested models are also presented.

• The Journal of the Korea Contents Association
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• v.15 no.2
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• pp.101-108
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• 2015

Location-sharing based service (LSBS) refers to a service that users share their location information with other users with whom friendship. At this time, the location information is shared through service provider, and then their position information is exposed to the service provider. The exposure of this personal position information to the service provider has raised a privacy problem, and thus privacy preserving mechanisms have been proposed to protect them. In this paper, we examine the types and features of the proposed location-sharing based services so far, and survey the research trend of privacy preserving mechanisms for them. Through the analysis on existing privacy preserving mechanisms, we present design factors for a privacy preserving mechanism for the current LSBS services, and suggest future work.

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

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.540-557
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• 2021

Privacy vulnerability of social networks is one of the major concerns for social science research and business analysis. Most existing studies which mainly focus on un-weighted network graph, have designed various privacy models similar to k-anonymity to prevent data disclosure of vertex attributes or relationships, but they may be suffered from serious problems of huge information loss and significant modification of key properties of the network structure. Furthermore, there still lacks further considerations of privacy protection for important sensitive edges in weighted social networks. To address this problem, this paper proposes a privacy preserving method to protect sensitive weighted edges. Firstly, the sensitive edges are differentiated from weighted edges according to the edge betweenness centrality, which evaluates the importance of entities in social network. Then, the perturbation operations are used to preserve the privacy of weighted social network by adding some pseudo-edges or modifying specific edge weights, so that the bottleneck problem of information flow can be well resolved in key area of the social network. Experimental results show that the proposed method can not only effectively preserve the sensitive edges with lower computation cost, but also maintain the stability of the network structures. Further, the capability of defending against malicious attacks to important sensitive edges has been greatly improved.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.81-88
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• 2023

In the present scenario, enormous amounts of data are produced every second. These data also contain private information from sources including media platforms, the banking sector, finance, healthcare, and criminal histories. Data mining is a method for looking through and analyzing massive volumes of data to find usable information. Preserving personal data during data mining has become difficult, thus privacy-preserving data mining (PPDM) is used to do so. Data perturbation is one of the several tactics used by the PPDM data privacy protection mechanism. In Perturbation, datasets are perturbed in order to preserve personal information. Both data accuracy and data privacy are addressed by it. This paper will explore and compare several perturbation strategies that may be used to protect data privacy. For this experiment, two perturbation techniques based on random projection and principal component analysis were used. These techniques include Improved Random Projection Perturbation (IRPP) and Enhanced Principal Component Analysis based Technique (EPCAT). The Naive Bayes classification algorithm is used for data mining approaches. These methods are employed to assess the precision, run time, and accuracy of the experimental results. The best perturbation method in the Nave-Bayes classification is determined to be a random projection-based technique (IRPP) for both the cardiovascular and hypothyroid datasets.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.443-446
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• 2006

RFID is the core of realizing ubiquitous environment. This is expected to improve economical effect through related industry revitalization, make-work, and so on, in the future, and to be linked to social see-through enhancement via national life change. However unchecked RFID use lets retailers collect unprecedented huge information and they link it to customer information database, so the voice of worry to bring about a result of trampling down consumer privacy doesn't make a negligible situation. Although RFID system is spreaded out socially, the servicing of law and system is not accomplished to protect individuals from personal information violation threat. At the same time, in ubiquitous computing environment, to protect individual information efficiently, from the step of planning and deciding this technology system, constitutional law, norm, the basic legal rights of the people, and so forth is to be considered. The objective of the research is to persent the privacy protection from the viewpoints of law on RFID.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1847-1870
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• 2021

With the development of information and communication technologies, especially wireless networks and cell phones, the e-voting system becomes popular as its cost-effectiveness, swiftness, scalability, and ecological sustainability. However, the current e-voting schemes are faced with the problem of privacy leakage and further cause worse vote-buying and voter-coercion problems. Moreover, in large-scale voting, some previous e-voting system encryption scheme with pairing operation also brings huge overhead pressure to the voting system. Thus, it is a vital problem to design a protocol that can protect voter privacy and simultaneously has high efficiency to guarantee the effective implementation of e-voting. To address these problems, our paper proposes an efficient unidirectional proxy re-encryption scheme that provides the re-encryption of vote content and the verification of users' identity. This function can be exactly applied in the e-voting system to protect the content of vote and preserve the privacy of the voter. Our proposal is proven to be CCA secure and collusion resistant. The detailed analysis also shows that our scheme achieves higher efficiency in computation cost and ciphertext size than the schemes in related fields.

• Journal of the Korean Society for Library and Information Science
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• v.46 no.4
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• pp.207-242
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• 2012

A library privacy policy describes the library's willingness to protect the library users' personal information, and also serves as a code of conduct for library staff. In recent years, cases of privacy invasion have been growing exponentially in society as a whole, including at the library, and therefore the establishment and application of a privacy policy is becoming more important. In this study, we try to develop and propose the optimal library privacy policy. For this purpose, we derived implications by analyzing the domestic and international privacy laws and guidelines, investigating invasion of privacy cases at home and abroad, and studying different library privacy policies from libraries around the world. The library privacy policy that we propose in this study was created to be a guideline for librarians when dealing with privacy issues and is library specific, diverging in many ways from privacy guidelines used in other fields.

• Informatization Policy
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• v.20 no.3
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• pp.63-85
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• 2013

Drawing on Protection Motivation Theory(PMT), this study attempts to clarify antecedents that influence the intention to protect individuals' privacy on the Internet. Protection motivation forms through individuals' cognitive appeal involving threat and efficacy. Then protection motivation causes privacy behavioral change. Protection motivation factors are established privacy trust and privacy risk, which are related to privacy attitude and belief. This proposed model is empirically analyzed by utilizing structural equation analysis(SEM). According to the result of the empirical analysis, it is founded that almost paths have statistically significant explanatory power except path from efficacy to privacy risk and path from privacy trust to privacy behavioral intention. This study shows powerful evidence of antecedent factors based on protection motivation of individuals' privacy behavioral intention in online environment.