• Journal of Computing Science and Engineering
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• v.5 no.3
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• pp.197-209
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• 2011

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.

• 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.

• Journal of KIISE:Databases
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• v.37 no.6
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• pp.324-332
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• 2010

The majority of event detection in real-time embedded sensor network systems is based on data fusion that uses noisy sensor data collected from complicated real-world environments. Current research has produced several excellent low-level mechanisms to collect sensor data and perform aggregation. However, solutions that enable these systems to provide real-time data processing using readings from heterogeneous sensors and subsequently detect complex events of interest in real-time fashion need further research. We are developing real-time event detection approaches which allow light-weight data fusion and do not require significant computing resources. Underlying the event detection framework is a collection of real-time monitoring and fusion mechanisms that are invoked upon the arrival of sensor data. The combination of these mechanisms and the framework has the potential to significantly improve the timeliness and reduce the resource requirements of embedded sensor networks. In addition to that, we discuss about a privacy that is foundation technique for trusted embedded sensor network system and explain anonymization technique to ensure privacy.