• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.2
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• pp.113-119
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• 2013

Face recognition techniques have been widely used for various areas including criminal identification due to their capability of easy implementing and user friendly interface. However, they have some drawbacks related to individual's privacy in case that his or her face information is divulged to illegal users. So, this paper proposed a novel method for protecting face template based on the real fuzzy vault. This proposed method has some advantages of regenerating a new face template when a registered face template is disclosed. Through implementing and testing the proposed method, we showed its validity and usefulness.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1049-1056
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• 2010

Recently RFID system has been adopted in various fields rapidly. However, we should solve the problem of privacy invasion that can be occurred by obtaining information of RFID Tag without any permission for popularization of RFID system. To solve these problems, There is the Ohkubo et al.'s Hash-Chain Scheme which is the safest method. However, this method has a problem that requesting lots of computing process because of creasing numbers of Tag. Therefore We, suggest SP-Division algorithm satisfied with all necessary security of Privacy Protection Scheme and decreased in Tag Identification Time in this paper. And this paper implemented it in time standard finding the first key among the data devided into each nodes. The length of Hash-Chain holds 1000, and the total number of SPs increases 1000, 2000, 3000, and 4000. Comparing tag identification time by the total number of SPs and the number of Nodes with single node, extending the number of nodes to 1, 2, 3 and 4, when the number of nodes is 2, 40% of Performance, when the number of nodes is 3, 56%, and when the number of nodes is 4, 71% is improved.

• Journal of Science and Technology Studies
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• v.3 no.2 s.6
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• pp.83-104
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• 2003

The use of DNA in identification is growing. The criminal DNA databases are in operation in some countries including the UK, Austria, Germany, and US. The militaries and law enforcement agencies in these countries have used the DNA profile. In Korea, DNA identification has been used in determining paternity and in criminal cases since the middle 1990's, and in recent years law enforcement agencies are promoting a national DNA database for identification. The DNA database threatens our civil liberties because of its potential to be used as an instrument of surveillance. Expanding the database puts increasing numbers of people on a 'list of suspects'. Nevertheless, there is little social concern about using DNA database for identification. This paper reviews social issues related to the establishment of DNA database and investigates the features of DNA profile and DNA Database establishment project promoted law enforcement agencies.

• Journal of Digital Convergence
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• v.17 no.11
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• pp.127-136
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• 2019

In the social media, information that users share with service providers can be divided into personal identification information such as gender and age and privacy-related information such as photos and comments. However, previous works on IS and service management have shed relatively little light on the difference of information-sharing decisions depending on the type of information. This study examines information-sharing decisions by separating the two types of information. A structural equation modeling method is used to test the research model based on a sample of 350 Facebook in South Korea. Analysis results show that self-expression, trust, and perceived security had a significant positive effect on both user's intentions to give personal identification information and their intentions to give privacy-related information. However, privacy concerns negatively affected their intentions to give personal identification and intention to give privacy-related information. The analysis results confirm that there was no difference between decision-making processes about sharing personal identification information and ones about sharing privacy-related information.

• Journal of Convergence for Information Technology
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• v.9 no.7
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• pp.100-105
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• 2019

As IoT Technology develops and Era of Hyperconnectivity comes, various kinds of customized services became available. As a next-generation display, a smart mirror accesses multimedia devices and provides various services, so it can serve as a social learning tool for the children and the old ones, as well as adults who need information. Smart Mirror must be able to identify users for individualized services. However, since the Smart Mirror is an easily accessible device, there is a possibility that information such as an individual's pattern and habit stored in the smart mirror may be exposed to the outside. Also, the other possibility of leakage of personal location information is through personal schedule or appointment stored in the smart mirror, and another possibility that privacy can be violated is through checking the health state via personal photographs. In this research, we propose a system that identify users by the information the users registered about their physique just like their face, one that provides individually customized service to users after identifying them, and one which provides minimal information and service for unauthenticated users.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.15 no.5
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• pp.59-71
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• 2005

A RFID (Radio Frequency Identification) system receives attention as the technology which can realize the ubiquitous computing environment. However, the feature of the RFID tags may bring about new threats to the security and privacy of individuals. Recently, Juels proposed the minimalist cryptography for very low-cost RFID tags, which is secure. but only under the impractical assumption such that an adversary is allowed to eavesdrop only the pre-defined number of sessions. In this paper, we propose a scheme to protect privacy for very low-cost RFID systems. The proposed protocol uses only bit-wise operations without my costly cryptographic function such as hashing, encryption which is secure which is secure against an adversary who is allowed to eavesdrop transmitted message in every session any impractical assumption. The proposed scheme also is more efficient since our scheme requires less datas as well as few number of computations than Juels's scheme.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.73-85
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• 2022

In the era of the 4th industrial revolution, where automation and connectivity are maximized with artificial intelligence, the importance of data collection and utilization for model update is increasing. In order to create a model using artificial intelligence technology, it is usually necessary to gather data in one place so that it can be updated, but this can infringe users' privacy. In this paper, we introduce federated learning, a distributed machine learning method that can update models in cooperation without directly sharing distributed stored data, and introduce a study to optimize distributed consensus among participants without an existing server. In addition, we propose a pattern and group-based distributed consensus optimization algorithm that uses an algorithm for generating patterns and groups based on the Kirkman Triple System, and performs parallel updates and communication. This algorithm guarantees more privacy than the existing distributed consensus optimization algorithm and reduces the communication time until the model converges.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.161-169
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• 2022

With the development of mobile devices and global positioning systems, various location-based services can be utilized, which collects user's location information and provides services based on it. In this process, there is a risk of personal sensitive information being exposed to the outside, and thus Geo-indistinguishability (Geo-Ind), which protect location privacy of LBS users by perturbing their true location, is widely used. However, owing to the data perturbation mechanism of Geo-Ind, it is hard to accurately obtain the density distribution of LBS users from the collection of perturbed location data. Thus, in this paper, we aim to develop a novel method which enables to effectively compute the user density distribution from perturbed location dataset collected under Geo-Ind. In particular, the proposed method leverages Expectation-Maximization(EM) algorithm to precisely estimate the density disribution of LBS users from perturbed location dataset. Experimental results on real world datasets show that our proposed method achieves significantly better performance than a baseline approach.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.4
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• pp.7-18
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• 2017

Electrical energy is one of the most important energy sources in modern society. Therefore, it is very important to control the supply and demand of electric power. However, the power consumption data needed to predict power demand may include the information about the private behavior of an individual, the analysis of which may raise privacy issues. In this paper, we propose a secure power demand forecasting method where regression analyses on power consumption data are conducted in a trusted execution environment provided by Intel SGX, keeping the power usage pattern of users private. We performed experiments using various regression equations and selected an equation which has the least error rate. We show that the average error rate of the proposed method is lower than those of the previous forecasting methods with privacy protection functionality.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1055-1065
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• 2023

Recently, various studies using deep reinforcement learning (deep RL) technology have been conducted to solve complex problems using big data collected at industrial internet of things. Deep RL uses reinforcement learning"s trial-and-error algorithms and cumulative compensation functions to generate and learn its own data and quickly explore neural network structures and parameter decisions. However, studies so far have shown that the larger the size of the learning data is, the higher are the memory usage and search time, and the lower is the accuracy. In this study, model-agnostic learning for efficient federated deep RL was utilized to solve privacy invasion by increasing robustness as 55.9% and achieve 97.8% accuracy, an improvement of 5.5% compared with the comparative optimization-based meta learning models, and to reduce the delay time by 28.9% on average.