• Smart Media Journal
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• v.10 no.2
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• pp.100-109
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• 2021

As the COVID-19 vaccination begins, it is necessary to safely store and manage the vaccination history for vaccinated people, as well as provide only the minimal information for the requested purpose, not in the form of all or nothing, to the institution requesting vaccination personal information. This paper proposes a scheme to safely store and manage the people's vaccination records in a non-forgeable blockchain, and to ensure that users provide only the minimal information necessary to the verifier from their vaccination personal information. A user authorizes the verifier to access the information he has consented with by entering the fingerprint on his smartphone, and in this process, no personal information or secrets can be exposed to an attacker. In addition, it is guaranteed that it is neither possible to impersonate the user nor to steal user personal information even in the case of theft or loss of the smartphone, or leakage of information from the vaccination history management institution. Using the scheme, users have no fear on external exposure of personal information and follow-up damage due to excessive information provision by giving out only the minimal information suited to the verifier.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1339-1355
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• 2023

Localization is a hot research spot for many areas, especially in the mobile robot field. Due to the weak signal of the global positioning system (GPS), the alternative schemes in an indoor environment include wireless signal transmitting and receiving solutions, laser rangefinder to build a map followed by a re-localization stage and visual positioning methods, etc. Among all wireless signal positioning techniques, Wi-Fi is the most common one. Wi-Fi access points are installed in most indoor areas of human activities, and smart devices equipped with Wi-Fi modules can be seen everywhere. However, the localization of a mobile robot using a Wi-Fi scheme usually lacks orientation information. Besides, the distance error is large because of indoor signal interference. Another research direction that mainly refers to laser sensors is to actively detect the environment and achieve positioning. An occupancy grid map is built by using the simultaneous localization and mapping (SLAM) method when the mobile robot enters the indoor environment for the first time. When the robot enters the environment again, it can localize itself according to the known map. Nevertheless, this scheme only works effectively based on the prerequisite that those areas have salient geometrical features. If the areas have similar scanning structures, such as a long corridor or similar rooms, the traditional methods always fail. To address the weakness of the above two methods, this work proposes a coarse-to-fine paradigm and an improved localization algorithm that utilizes Wi-Fi to assist the robot localization in a geometrically similar environment. Firstly, a grid map is built by using laser SLAM. Secondly, a fingerprint database is built in the offline phase. Then, the RSSI values are achieved in the localization stage to get a coarse localization. Finally, an improved particle filter method based on the Wi-Fi signal values is proposed to realize a fine localization. Experimental results show that our approach is effective and robust for both global localization and the kidnapped robot problem. The localization success rate reaches 97.33%, while the traditional method always fails.

• The KIPS Transactions:PartD
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• v.10D no.2
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• pp.309-316
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• 2003

In this paper, we propose a new watermarking method based on spread spectrum and a semi-fingerprinting system architecture that can be built using our robust watermarking method. Especially, we describe a method that extends the application area of watermarking technology to more practical application domains by applying the watermarking technology that has been focused mainly on copyright protection to fingerprinting area. Our proposed watermarking scheme uses the method that inserts more data by using random number shifting method. We improved the reliability of acquired data with 20-bits CRC code and 60-bits inserted information. In addition, we designed the system architecture based on the recommendation of cIDf (content ID forum) in order to apply the system on the semi-fingerprinting area.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.6
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• pp.95-105
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• 2003

A novel reference point detection method is proposed by exploiting tile gradient probabilistic model that captures the curvature information of fingerprint. The detection of reference point is accomplished through searching and locating the points of occurrence of the most evenly distributed gradient in a probabilistic sense. The uniformly distributed gradient texture represents either the core point itself or those of similar points that can be used to establish the rigid reference from which to map the features for recognition. Key benefits are reductions in preprocessing and consistency of locating the same points as the reference points even when processing arch type fingerprints. Moreover, the new feature extraction method is proposed by improving the existing feature extraction using filterbank method. Experimental results indicate the superiority of tile proposed scheme in terms of computational time in feature extraction and verification rate in various noisy environments. In particular, the proposed gradient probabilistic model achieved 49% improvement under ambient noise, 39.2% under brightness noise and 15.7% under a salt and pepper noise environment, respectively, in FAR for the arch type fingerprints. Moreover, a reduction of 0.07sec in reference point detection time of the GPM is shown possible compared to using the leading the poincare index method and a reduction of 0.06sec in code extraction time of the new filterbank mettled is shown possible compared to using the leading the existing filterbank method.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.4
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• pp.76-86
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• 2013

As the basic Kalman filter is limited to be used for indoor navigation, and particle filters incur serious computational overhead, especially in mobile devices, we propose an adaptive hybrid filter for WiFi-based indoor positioning systems. The hybrid filter utilizes the same prediction framework of the basic Kalman filter, and it adopts the notion of particle filters only using a small number of particles. Restricting the predicts of a moving object to a small number of particles on a way network and substituting a dynamic weighting scheme for Kalman gain are the key features of the filter. The adaptive hybrid filter showed significantly better accuracy than the basic Kalman filter did, and it showed greatly improved performance in processing time and slightly better accuracy compared with a particle filter.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.4
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• pp.470-476
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• 2019

The pattern locking technique applied to smart phones is a locking technique that many people use conveniently. However, the safety of pattern locking techniques is very low compared with other techniques. The pattern locking technique is vulnerable to a shoulder surfing attack, which is based on the user's input and can be interpreted by looking at the movement of the shoulder, and the smudge attack is also vulnerable due to fingerprint drag marks remaining on the mobile phone pad. Therefore, in this paper, we want to add a new security method to check the pressed time by using a thread in the pattern locking scheme to secure the vulnerability. It is divided into short, middle, and long click according to the pressing time at each point. When dragging using the technique, security performance enhances $3^n$ tiems. Therefore, even if dragging in the same 'ㄱ' manner, it becomes a completely different pattern depending on the pressing time at each point.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.69-75
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• 2023

Small communication devices used in the Internet of Things are vulnerable to various hacking because they do not apply advanced encryption techniques due to their low memory capacity or slow computation speed. In order to increase the authentication reliability of small-sized transmitters operating in 433MHz band, we introduce an RF fingerprint and adopt a convolutional neural network (CNN) as a classification algorithm. The preamble signal transmitted by each transmitter are extracted and collected using software-defined-radio to constitute a training data set, which is used for training the CNN. We tested identification of 20 transmitters in four different scenarios and obtained high identification accuracy. In particular, the accuracy of 95.8% and 92.6% was obtained, respectively in the scenario where the test was performed at a location different from the transmitter's location at the time of collecting training data, and in the scenario where the transmitter moves at walking speed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.2
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• pp.334-348
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• 2015

Widespread use of smart devices accompanies increase of use of access point (AP), which enables the connection to the wireless network. If the appropriate security is not served when a user tries to connect the wireless network through an AP, various security problems can arise due to the rogue APs. In this paper, we are going to examine the threat by evil-twin, which is a kind of rogue APs. Most of recent researches for detecting rogue APs utilize the measured time difference, such as round trip time (RTT), between the evil-twin and authorized APs. These methods, however, suffer from the low detection rate in the network congestion. Due to these reasons, in this paper, we suggest a new factor, packet inter-arrival time (PIAT), in order to detect evil-twins. By using both RTT and PIAT as the learning factors for the support vector machine (SVM), we determine the non-linear metric to classify evil-twins and authorized APs. As a result, we can detect evil-twins with the probability of up to 96.5% and at least 89.75% even when the network is congested.