• Journal of Internet Computing and Services
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• v.11 no.4
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• pp.23-32
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• 2010

In this paper, we first propose the security enhancing of authentication protocol for Hash based RFID tag, and then a digital Codec for RFID tag is designed based on the proposed authentication protocol. The protocol is based on a three-way challenge response authentication protocol between the tags and a back-end server. In order to realize a secure cryptographic authentication mechanism, we modify three types of the protocol packets which defined in the ISO/IEC 18000-3 standard. Thus active attacks such as the Man-in-the-middle and Replay attacks can be easily protected. In order to verify effectiveness of the proposed protocol, a digital Codec for RFID tag is designed using Verilog HDL, and also synthesized using Synopsys Design Compiler with Hynix $0.25\;{\mu}m$ standard-cell library. Through security analysis and comparison result, we will show that the proposed scheme has better performance in user data confidentiality, tag anonymity, Man-in-the-middle attack prevention, replay attack, forgery resistance and location tracking.

• Journal of Convergence Society for SMB
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• v.1 no.1
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• pp.29-37
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• 2011

As the area of informatization has been expanding followed by the development of information communication technology, cloud computing which can use infra sources like server, storage, and network in IT area as an efficient service whenever and wherever skyrockets. But users who use cloud computing technology may have some problems like exposure personal data, surveillance on person, and process on commercial purpose on their personal data. This paper proposes a security technique which protect user's privacy by creating imaginary user information not to be used by other people. The proposed technique virtualizes user's information as an anonymity value not to let other people know user's identity by combining PIN code with it and guarantees user's anonymity. Also it can manage and certificate personal information that is important in cloud computing, so that it can solve security problem of cloud computing which centers all informations. Therefore this paper can assist upgrading of the level of information of poor SMBs through safe use of cloud computing.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.1
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• pp.81-87
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• 2012

The current defacing method for keeping an anonymity of brain images damages the integrity of a precise brain analysis due to over removal, although it maintains the patients' privacy. A novel method has been developed to create an anonymous face model while keeping the voxel values of an image exactly the same as that of the original one. The method contains two steps: construction of a mockup brain template from ten normalized brain images and a substitution of the mockup brain to the brain image. A level set segmentation algorithm is applied to segment a scalp-skull apart from the whole brain volume. The segmented mockup brain is coregistered and normalized to the subject brain image to create an anonymous face model. The validity of this modification is tested through comparing the intensity of voxels inside a brain area from the mockup brain with the original brain image. The result shows that the intensity of voxels inside from the mockup brain is same as ones from an original brain image, while its anonymity is guaranteed.

• Journal of Korea Game Society
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• v.15 no.4
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• pp.93-110
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• 2015

This study aimed at identifying user recognition of the types and causes of online game trolling through in-depth interview with users of . Online game trolling refers to anti-social behaviors to do acts provoking other users to anger intentionally so induce their actual reaction in online games. Types of online game trolling contained flaming, griefing, unskilled player and lack of understanding on user's rule. And users are recognizing as problems that anonymity is excessively high in the game, a single game has undue significance, team play is given too much emphasis in a situation lacking in social cohesivenessk as the structural causes of trolling in . Accordingly, in order to decrease online game trolling, it is urgent to improve the game-structured layer causing trolling rather than regulate trollers only.

• Journal of KIISE:Databases
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• v.37 no.4
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• pp.209-219
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• 2010

Online social network services that are rapidly growing recently store tremendous data and analyze them for many research areas. To enhance the effectiveness of information, companies or public institutions publish their data and utilize the published data for many purposes. However, a social network containing information of individuals may cause a privacy disclosure problem. Eliminating identifiers such as names is not effective for the privacy protection, since private information can be inferred through the structural information of a social network. In this paper, we consider a new complex attack type that uses both the content and structure information, and propose a model, $\ell$-degree diversity, for the privacy preserving publication of the social network data against such attacks. $\ell$-degree diversity is the first model for applying $\ell$-diversity to social network data publication and through the experiments it shows high data preservation rate.

• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.109-117
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• 2019

Recently, the invasion of privacy in medical information has been issued following the interest in the secondary use of mass medical information. The mass medical information is very useful information that can be used in various fields such as disease research and prevention. However, due to privacy laws such as the Privacy Act and Medical Law, this information, including patients' or health professionals' personal information, is difficult to utilize as a secondary use of mass information. To do these problem, various methods such as k-anonymity, l-diversity and differential-privacy that can be utilized while protecting privacy have been developed and utilized in this field. In this paper, we discuss the differential privacy processing of the various methods that have been studied so far, and discuss the problems of differential privacy using Laplace noise and the previously proposed differential privacy. Finally, we propose a new scheme to solve the existing problem by adding a 1-bit status field to the last column of a given data set to confirm the response to queries from analysts.

• Smart Media Journal
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• v.8 no.1
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• pp.9-18
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• 2019

In the era of the 4th industrial revolution, the big data industry is gathering attention for new business models in the public and private sectors by utilizing various information collected through the internet and mobile. However, although the big data integration and analysis are performed with de-identification techniques, there is still a risk that personal privacy can be exposed. Recently, there are many studies to invent effective methods to maintain the value of data without disclosing personal information. In this paper, a personal information protection system is investigated to boost big data utilization in industrial sectors, such as healthcare and agriculture. The criteria for evaluating the de-identification adequacy of personal information and the protection scope of personal information should be differently applied for each industry. In the field of personal sensitive information-oriented healthcare sector, the minimum value of k-anonymity should be set to 5 or more, which is the average value of other industrial sectors. In agricultural sector, it suggests the inclusion of companion dogs or farmland information as sensitive information. Also, it is desirable to apply the demonstration steps to each region-specific industry.

• 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.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.2
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• pp.311-319
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• 2017

Recently invasion of privacy problem in medical information have been issued following the interest in secondary use of large medical information. These large medical information is very useful information that can be used in various fields such as disease research and prevention. However, due to the privacy laws such as Privacy Act and Medical Law, these informations including patients or health professionals' personal information are difficult to utilize secondary. Accordingly, various methods such as k-anonymity, l-diversity and differential-privacy that can be utilized while protecting privacy have been developed and utilized in this field. In this paper, we study differential privacy processing procedure, one of various methods, and find out about the differential privacy problem using Laplace noise. Finally, we propose a new method using the Shamir's secret sharing method and symemetric key encryption algorithm such as AES for this problem.

• Journal of the Korea Society of Computer and Information
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• v.29 no.10
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• pp.207-218
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• 2024

In this paper, we propose a model for identifying drug trafficking organizations and assessing their scale by collecting drug promotional tweets from the social media platform 'X,' with a focus on investigating drug crimes that frequently occur among teenagers and young adults. Recently, various cyber crimes, such as drug distribution, illegal gambling, and sex offense, have been on the rise, exploiting the anonymity provided by social media. Drug trafficking organizations, in particular, operate in a decentralized cell structure, where each member receives anonymous instructions regarding only their specific role and is not directly connected to other members. To track these types of crimes, we designed experimental scenarios using various clustering algorithms, such as K-means Clustering and Spectral Clustering, alongside text embedding models like BERT (Bidirectional Encoder Representations from Transformers) and GloVe (Global Vectors for Word Representation). Furthermore, the clustering results derived from each scenario are validated using Jaccard Similarity and a full-scale investigation. We then analyze tweet clusters identified as the same drug organization across all scenarios, prioritizing the identification of high-priority accounts for cyber investigations.