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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
> Journal Vol & Issue
Journal of Computing Science and Engineering
Journal Basic Information
Journal DOI :
Korean Institute of Information Scientists and Engineers
Editor in Chief :
In-Sup Lee / Il-Yeol Song / Jong C. Park / Tae-Whan Kim
Volume & Issues
Volume 5, Issue 4 - Dec 2011
Volume 5, Issue 3 - Sep 2011
Volume 5, Issue 2 - Jun 2011
Volume 5, Issue 1 - Mar 2011
Selecting the target year
A Fast Algorithm for Korean Text Extraction and Segmentation from Subway Signboard Images Utilizing Smartphone Sensors
Milevskiy, Igor ; Ha, Jin-Young ;
Journal of Computing Science and Engineering, volume 5, issue 3, 2011, Pages 161~166
DOI : 10.5626/JCSE.2011.5.3.161
We present a fast algorithm for Korean text extraction and segmentation from subway signboards using smart phone sensors in order to minimize computational time and memory usage. The algorithm can be used as preprocessing steps for optical character recognition (OCR): binarization, text location, and segmentation. An image of a signboard captured by smart phone camera while holding smart phone by an arbitrary angle is rotated by the detected angle, as if the image was taken by holding a smart phone horizontally. Binarization is only performed once on the subset of connected components instead of the whole image area, resulting in a large reduction in computational time. Text location is guided by user`s marker-line placed over the region of interest in binarized image via smart phone touch screen. Then, text segmentation utilizes the data of connected components received in the binarization step, and cuts the string into individual images for designated characters. The resulting data could be used as OCR input, hence solving the most difficult part of OCR on text area included in natural scene images. The experimental results showed that the binarization algorithm of our method is 3.5 and 3.7 times faster than Niblack and Sauvola adaptive-thresholding algorithms, respectively. In addition, our method achieved better quality than other methods.
Limiting Attribute Disclosure in Randomization Based Microdata Release
Guo, Ling ; Ying, Xiaowei ; Wu, Xintao ;
Journal of Computing Science and Engineering, volume 5, issue 3, 2011, Pages 169~182
DOI : 10.5626/JCSE.2011.5.3.169
Privacy preserving microdata publication has received wide attention. In this paper, we investigate the randomization approach and focus on attribute disclosure under linking attacks. We give efficient solutions to determine optimal distortion parameters, such that we can maximize utility preservation while still satisfying privacy requirements. We compare our randomization approach with l-diversity and anatomy in terms of utility preservation (under the same privacy requirements) from three aspects (reconstructed distributions, accuracy of answering queries, and preservation of correlations). Our empirical results show that randomization incurs significantly smaller utility loss.
Privacy Disclosure and Preservation in Learning with Multi-Relational Databases
Guo, Hongyu ; Viktor, Herna L. ; Paquet, Eric ;
Journal of Computing Science and Engineering, volume 5, issue 3, 2011, Pages 183~196
DOI : 10.5626/JCSE.2011.5.3.183
There has recently been a surge of interest in relational database mining that aims to discover useful patterns across multiple interlinked database relations. It is crucial for a learning algorithm to explore the multiple inter-connected relations so that important attributes are not excluded when mining such relational repositories. However, from a data privacy perspective, it becomes difficult to identify all possible relationships between attributes from the different relations, considering a complex database schema. That is, seemingly harmless attributes may be linked to confidential information, leading to data leaks when building a model. Thus, we are at risk of disclosing unwanted knowledge when publishing the results of a data mining exercise. For instance, consider a financial database classification task to determine whether a loan is considered high risk. Suppose that we are aware that the database contains another confidential attribute, such as income level, that should not be divulged. One may thus choose to eliminate, or distort, the income level from the database to prevent potential privacy leakage. However, even after distortion, a learning model against the modified database may accurately determine the income level values. It follows that the database is still unsafe and may be compromised. This paper demonstrates this potential for privacy leakage in multi-relational classification and illustrates how such potential leaks may be detected. We propose a method to generate a ranked list of subschemas that maintains the predictive performance on the class attribute, while limiting the disclosure risk, and predictive accuracy, of confidential attributes. We illustrate and demonstrate the effectiveness of our method against a financial database and an insurance database.
Anonymizing Graphs Against Weight-based Attacks with Community Preservation
Li, Yidong ; Shen, Hong ;
Journal of Computing Science and Engineering, volume 5, issue 3, 2011, Pages 197~209
DOI : 10.5626/JCSE.2011.5.3.197
The increasing popularity of graph data, such as social and online communities, has initiated a prolific research area in knowledge discovery and data mining. As more real-world graphs are released publicly, there is growing concern about privacy breaching for the entities involved. An adversary may reveal identities of individuals in a published graph, with the topological structure and/or basic graph properties as background knowledge. Many previous studies addressing such attacks as identity disclosure, however, concentrate on preserving privacy in simple graph data only. In this paper, we consider the identity disclosure problem in weighted graphs. The motivation is that, a weighted graph can introduce much more unique information than its simple version, which makes the disclosure easier. We first formalize a general anonymization model to deal with weight-based attacks. Then two concrete attacks are discussed based on weight properties of a graph, including the sum and the set of adjacent weights for each vertex. We also propose a complete solution for the weight anonymization problem to prevent a graph from both attacks. In addition, we also investigate the impact of the proposed methods on community detection, a very popular application in the graph mining field. Our approaches are efficient and practical, and have been validated by extensive experiments on both synthetic and real-world datasets.
Uncertainty for Privacy and 2-Dimensional Range Query Distortion
Sioutas, Spyros ; Magkos, Emmanouil ; Karydis, Ioannis ; Verykios, Vassilios S. ;
Journal of Computing Science and Engineering, volume 5, issue 3, 2011, Pages 210~222
DOI : 10.5626/JCSE.2011.5.3.210
In this work, we study the problem of privacy-preservation data publishing in moving objects databases. In particular, the trajectory of a mobile user in a plane is no longer a polyline in a two-dimensional space, instead it is a two-dimensional surface of fixed width
defines the semi-diameter of the minimum spatial circular extent that must replace the real location of the mobile user on the XY-plane, in the anonymized (kNN) request. The desired anonymity is not achieved and the entire system becomes vulnerable to attackers, since a malicious attacker can observe that during the time, many of the neighbors` ids change, except for a small number of users. Thus, we reinforce the privacy model by clustering the mobile users according to their motion patterns in (u,
) plane, where u and
define the velocity measure and the motion direction (angle) respectively. In this case, the anonymized (kNN) request looks up neighbors, who belong to the same cluster with the mobile requester in (u,
) space: Thus, we know that the trajectory of the k-anonymous mobile user is within this surface, but we do not know exactly where. We transform the surface`s boundary poly-lines to dual points and we focus on the information distortion introduced by this space translation. We develop a set of efficient spatiotemporal access methods and we experimentally measure the impact of information distortion by comparing the performance results of the same spatiotemporal range queries executed on the original database and on the anonymized one.
Secure Blocking + Secure Matching
Karakasidis, Alexandros ; Verykios, Vassilios S. ;
Journal of Computing Science and Engineering, volume 5, issue 3, 2011, Pages 223~235
DOI : 10.5626/JCSE.2011.5.3.223
Performing approximate data matching has always been an intriguing problem for both industry and academia. This task becomes even more challenging when the requirement of data privacy rises. In this paper, we propose a novel technique to address the problem of efficient privacy-preserving approximate record linkage. The secure framework we propose consists of two basic components. First, we utilize a secure blocking component based on phonetic algorithms statistically enhanced to improve security. Second, we use a secure matching component where actual approximate matching is performed using a novel private approach of the Levenshtein Distance algorithm. Our goal is to combine the speed of private blocking with the increased accuracy of approximate secure matching.
Managing Sensor Data in Ambient Assisted Living
Nugent, C.D. ; Galway, L. ; Chen, L. ; Donnelly, M.P. ; Mcclean, S.I. ; Zhang, S. ; Scotney, B.W. ; Parr, G. ;
Journal of Computing Science and Engineering, volume 5, issue 3, 2011, Pages 237~245
DOI : 10.5626/JCSE.2011.5.3.237
The use of technology within the home has gained wide spread acceptance as one possible approach to be used in addressing the challenges of an ageing society. A number of rudimentary assistive solutions are now being deployed in real settings but with the introduction of these technology-orientated services come a number of challenges, which to date are still largely unsolved. At a fundamental level, the management and processing of the large quantities of data generated from multiple sensors is recognised as one of the most significant challenges. This paper aims to present an overview of the types of sensor technologies used within Ambient Assisted Living. Subsequently, through presentation of a series of case studies, the paper will demonstrate how the practical integration of multiple sources of sensor data can be used to improve the overall concept and applications of Ambient Assisted Living.
Wearable Intelligent Systems for E-Health
Poon, Carmen C.Y. ; Liu, Qing ; Gao, Hui ; Lin, Wan-Hua ; Zhang, Yuan-Ting ;
Journal of Computing Science and Engineering, volume 5, issue 3, 2011, Pages 246~256
DOI : 10.5626/JCSE.2011.5.3.246
Due to the increasingly aging population, there is a rising demand for assistive living technologies for the elderly to ensure their health and well-being. The elderly are mostly chronic patients who require frequent check-ups of multiple vital signs, some of which (e.g., blood pressure and blood glucose) vary greatly according to the daily activities that the elderly are involved in. Therefore, the development of novel wearable intelligent systems to effectively monitor the vital signs continuously over a 24 hour period is in some cases crucial for understanding the progression of chronic symptoms in the elderly. In this paper, recent development of Wearable Intelligent Systems for e-Health (WISEs) is reviewed, including breakthrough technologies and technical challenges that remain to be solved. A novel application of wearable technologies for transient cardiovascular monitoring during water drinking is also reported. In particular, our latest results found that heart rate increased by 9 bpm (P < 0.001) and pulse transit time was reduced by 5 ms (P < 0.001), indicating a possible rise in blood pressure, during swallowing. In addition to monitoring physiological conditions during daily activities, it is anticipated that WISEs will have a number of other potentially viable applications, including the real-time risk prediction of sudden cardiovascular events and deaths.
A Survey of Transfer and Multitask Learning in Bioinformatics
Xu, Qian ; Yang, Qiang ;
Journal of Computing Science and Engineering, volume 5, issue 3, 2011, Pages 257~268
DOI : 10.5626/JCSE.2011.5.3.257
Machine learning and data mining have found many applications in biological domains, where we look to build predictive models based on labeled training data. However, in practice, high quality labeled data is scarce, and to label new data incurs high costs. Transfer and multitask learning offer an attractive alternative, by allowing useful knowledge to be extracted and transferred from data in auxiliary domains helps counter the lack of data problem in the target domain. In this article, we survey recent advances in transfer and multitask learning for bioinformatics applications. In particular, we survey several key bioinformatics application areas, including sequence classification, gene expression data analysis, biological network reconstruction and biomedical applications.
STEPSTONE: An Intelligent Integration Architecture for Personal Tele-Health
Helal, Sumi ; Bose, Raja ; Chen, Chao ; Smith, Andy ; De Deugd, Scott ; Cook, Diane ;
Journal of Computing Science and Engineering, volume 5, issue 3, 2011, Pages 269~281
DOI : 10.5626/JCSE.2011.5.3.269
STEPSTONE is a joint industry-university project to create open source technology that would enable the scalable, "friction-free" integration of device-based healthcare solutions into enterprise systems using a Service Oriented Architecture (SOA). Specifically, STEPSTONE defines a first proposal to a Service Oriented Device Architecture (SODA) framework, and provides for initial reference implementations. STEPSTONE also intends to encourage a broad community effort to further develop the framework and its implementations. In this paper, we present SODA, along with two implementation proposals of SODA`s device integration. We demonstrate the ease by which SODA was used to develop an end-to-end personal healthcare monitoring system. We also demonstrate the ease by which the STEPSTONE system was extended by other participants - Washington State University - to include additional devices and end user interfaces. We show clearly how SODA and therefore SODA devices make integration almost automatic, replicable, and scalable. This allows telehealth system developers to focus their energy and attention on the system functionality and other important issues, such as usability, privacy, persuasion and outcome assessment studies.