• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1717-1732
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• 2015

The widespread use of location-based services (LBSs), which allows untrusted service provider to collect large number of user request records, leads to serious privacy concerns. In response to these issues, a number of LBS privacy protection mechanisms (LPPMs) have been recently proposed. However, the evaluation of these LPPMs usually disregards the background knowledge that the adversary may possess about users' contextual information, which runs the risk of wrongly evaluating users' query privacy. In this paper, we address these issues by proposing a generic formal quantification framework,which comprehensively contemplate the various elements that influence the query privacy of users and explicitly states the knowledge that an adversary might have in the context of query privacy. Moreover, a way to model the adversary's attack on query privacy is proposed, which allows us to show the insufficiency of the existing query privacy metrics, e.g., k-anonymity. Thus we propose two new metrics: entropy anonymity and mutual information anonymity. Lastly, we run a set of experiments on datasets generated by network based generator of moving objects proposed by Thomas Brinkhoff. The results show the effectiveness and efficient of our framework to measure the LPPM.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3328-3349
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• 2020

With rapid development of ubiquitous computing and location-based services (LBSs), human trajectory data and associated activities are increasingly easily recorded. Inappropriately publishing trajectory data may leak users' privacy. Therefore, we study publishing trajectory data while preserving privacy, denoted privacy-preserving activity trajectories publishing (PPATP). We propose S-PPATP to solve this problem. S-PPATP comprises three steps: modeling, algorithm design and algorithm adjustment. During modeling, two user models describe users' behaviors: one based on a Markov chain and the other based on the hidden Markov model. We assume a potential adversary who intends to infer users' privacy, defined as a set of sensitive information. An adversary model is then proposed to define the adversary's background knowledge and inference method. Additionally, privacy requirements and a data quality metric are defined for assessment. During algorithm design, we propose two publishing algorithms corresponding to the user models and prove that both algorithms satisfy the privacy requirement. Then, we perform a comparative analysis on utility, efficiency and speedup techniques. Finally, we evaluate our algorithms through experiments on several datasets. The experiment results verify that our proposed algorithms preserve users' privay. We also test utility and discuss the privacy-utility tradeoff that real-world data publishers may face.

• Journal of Digital Convergence
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• v.16 no.10
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• pp.445-452
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• 2018

In a world where everything in life is being digitized, the amount of data is increasing exponentially. These data are processed into new data through collection and analysis. New data is used for a variety of purposes in hospitals, finance, and businesses. However, since existing data contains sensitive information of individuals, there is a fear of personal privacy exposure during collection and analysis. As a solution, there is privacy-preserving data mining (PPDM) technology. PPDM is a method of extracting useful information from data while preserving privacy. In this paper, we investigate PPDM and analyze various measures for evaluating the privacy and utility of data.

• Journal of the Korea Society of Computer and Information
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• v.18 no.3
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• pp.47-57
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• 2013

Location privacy has been a serious concern for mobile users who use location-based services to acquire geographical location continuously. Spatial cloaking technique is a well-known privacy preserving method, which blurs an exact user location into a cloaked area to meet privacy requirements. However, cloaking for continuous moving object suffers from cloaked area size problem as it is unlikely for all objects travel in the same direction. In this paper, we propose a grid-based privacy preservation method with an improved Earth Mover's Distance(EMD) metric weight update scheme for semantic cloaking. We also define a representative cloaking area which protects continuous location privacy for moving users. Experimental implementation and evaluation exhibit that our proposed method renders good efficiency and scalability in cloaking processing time and area size control. We also show that our proposed method outperforms the existing method by successfully protects location privacy of continuous moving objects against various adversaries.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2006.06a
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• pp.354-360
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• 2006

The purpose of this paper is to propose a securely personalized context-aware cooperative query that supports a multi-level data abstraction hierarchy and conceptual distance metric among data values, while considering privacy concerns around user context awareness. The conceptual distance expresses a semantic similarity among data values with a quantitative measure, and thus the conceptual distance enables query results to be ranked. To show the feasibility of the methodology proposed in this paper we have implemented a prototype system in the area of site search in a large-scale shopping mall.

• Journal of Korea Multimedia Society
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• v.16 no.10
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• pp.1156-1162
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• 2013

Face verification has been widely studied during the past two decades. One of the challenges is the rising concern about the security and privacy of the template database. In this paper, we propose a secure face verification system which generates a unique secure cryptographic key from a face template. The face images are processed to produce face templates or codes to be utilized for the encryption and decryption tasks. The result identity data is encrypted using Advanced Encryption Standard (AES). Distance metric naming hamming distance and Euclidean distance are used for template matching identification process, where template matching is a process used in pattern recognition. The proposed system is tested on the ORL, YALEs, and PKNU face databases, which contain 360, 135, and 54 training images respectively. We employ Principle Component Analysis (PCA) to determine the most discriminating features among face images. The experimental results showed that the proposed distance measure was one the promising best measures with respect to different characteristics of the biometric systems. Using the proposed method we needed to extract fewer images in order to achieve 100% cumulative recognition than using any other tested distance measure.

• Journal of Korean Society of societal Security
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• v.1 no.4
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• pp.33-40
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• 2008

Personal information security has been as risk ever since the development of information technology increased its internet use. As personal information security is compromised there will be a rise in personal privacy conflicts and this will become an important social issue. The following research is a presentation of the warning map for risk monitoring on personal information security. First, the personal information security process is identified then defined. Second, in order to achieve the personal information security's objective, a survey was taken and the data was collected. Third, factor in the Fishbone Diagram's analysis and figure out the key indicators that include metric and threshold. Last, develop the warning map which has the matrix table composed of the process and the risk. It displays the warning based on the threshold and the value of key indicators related to risks.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.1-11
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• 2023

Federated learning (FL) is a ground breaking machine learning paradigm that allow smultiple participants to collaboratively train models in a cloud environment, all while maintaining the privacy of their raw data. This approach is in valuable in applications involving sensitive or geographically distributed data. However, one of the challenges in FL is dealing with heterogeneous and non-independent and identically distributed (non-IID) data across participants, which can result in suboptimal model performance compared to traditionalmachine learning methods. To tackle this, we introduce FedGCD, a novel FL algorithm that employs Graph Neural Network (GNN)-based community detection to enhance model convergence in federated settings. In our experiments, FedGCD consistently outperformed existing FL algorithms in various scenarios: for instance, in a non-IID environment, it achieved an accuracy of 0.9113, a precision of 0.8798,and an F1-Score of 0.8972. In a semi-IID setting, it demonstrated the highest accuracy at 0.9315 and an impressive F1-Score of 0.9312. We also introduce a new metric, nonIIDness, to quantitatively measure the degree of data heterogeneity. Our results indicate that FedGCD not only addresses the challenges of data heterogeneity and non-IIDness but also sets new benchmarks for FL algorithms. The community detection approach adopted in FedGCD has broader implications, suggesting that it could be adapted for other distributed machine learning scenarios, thereby improving model performance and convergence across a range of applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.4766-4786
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• 2016

Looking for missing companions who are out of touch in public places might suffer a long and painful process. With the help of mobile crowdsourcing, the missing person's location may be reported in a short time. In this paper, we propose MissingFound, an assistant system that applies mobile crowdsourcing for finding missing companions. Discovering valuable users who have chances to see the missing person is the most important task of MissingFound but also a big challenge with the requirements of saving battery and protecting users' location privacy. A customized metric is designed to measure the probability of seeing, according to users' movement traces represented by WiFi RSSI fingerprints. Since WiFi RSSI fingerprints provide no knowledge of users' physical locations, the computation of probability is too complex for practical use. By parallelizing the original sequential algorithms under MapReduce framework, the selecting process can be accomplished within a few minutes for 10 thousand users with records of several days. Experimental evaluation with 23 volunteers shows that MissingFound can select out the potential witnesses in reality and achieves a high accuracy (76.75% on average). We believe that MissingFound can help not only find missing companions, but other public services (e.g., controlling communicable diseases).

• The Journal of Society for e-Business Studies
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• v.24 no.1
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• pp.121-137
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• 2019

De-identification is a method by which the remaining information can not be referred to a specific individual by removing the personal information from the data set. As a result, de-identification can lower the exposure risk of personal information that may occur in the process of collecting, processing, storing and distributing information. Although there have been many studies in de-identification algorithms, protection models, and etc., most of them are limited to structured data, and there are relatively few considerations on de-identification of unstructured data. Especially, in the medical field where the unstructured text is frequently used, many people simply remove all personally identifiable information in order to lower the exposure risk of personal information, while admitting the fact that the data utility is lowered accordingly. This study proposes a new method to perform de-identification by applying the k-anonymity protection model targeting unstructured text in the medical field in which de-identification is mandatory because privacy protection issues are more critical in comparison to other fields. Also, the goal of this study is to propose a new utility metric so that people can comprehend de-identified data set utility intuitively. Therefore, if the result of this research is applied to various industrial fields where unstructured text is used, we expect that we can increase the utility of the unstructured text which contains personal information.