MoonBae Song

A Study on the Secure User Profiling Structure and Procedure for Home Healthcare Systems

Despite of various benefits such as a convenience and efficiency, home healthcare systems have some inherent security risks that may cause a serious leak on personal health information. This work presents a Secure User Profiling Structure which has the patient information including their health information. A patient and a hospital keep it at that same time, they share the updated data. While they share the data and communicate, the data can be leaked. To solve the security problems, a secure communication channel with a hash function and an One-Time Password between a client and a hospital should be established and to generate an input value to an OTP, it uses a dual hash-function. This work presents a dual hash function-based approach to generate the One-Time Password ensuring a secure communication channel with the secured key. In result, attackers are unable to decrypt the leaked information because of the secured key; in addition, the proposed method outperforms the existing methods in terms of computation cost.

A privacy-preserving continuous location monitoring system for location-based services

To protect users’ private locations in location-based services, various location anonymization techniques have been proposed. The most commonly used technique is spatial cloaking, which organizes users’ exact locations into cloaked regions (CRs). This satisfies the K-anonymity requirement; that is, the querier is not distinguishable among K users within the CR. However, the practicality of cloaking techniques is limited due to the lack of privacy-preserving query processing capacity, for example, providing answers to the users spatial queries based on knowledge of the users cloaked location rather than the exact location. This paper proposes a cloaking system model called anonymity of motion vectors (AMV) that provides anonymity for spatial queries. The proposed AMV minimizes the CR of a mobile user using motion vectors. In addition, the AMV creates a ranged search area that includes the nearest neighbor (NN) objects to the querier who issued a CR-based query. The effectiveness of the proposed AMV is demonstrated in simulated experiments.

Spatial indexing for massively update intensive applications

Supporting high update throughput is important to provide real-time location information for many applications, which involve moving objects, such as security, military, and environmental monitoring. We present an R-tree-based index structure with a family of update strategies for supporting high update throughput. It groups updates in the update buffer, and bulk-applies them into the R-tree. Extensive empirical studies have shown that it outperforms existing techniques by 2-5 times. In our experimental environment, moreover, it is able to provide the real-time update capability, while its competitors fail to do so.

Low-cost two-hop anchor node-based distributed range-free localization in wireless sensor networks

Due to the fact that most of the sensor network applications are based on the location information of sensor nodes, localization is an essential research area. Some localization schemes in the literature require sensor nodes to have additional devices to measure the distance or angle between two sensor nodes, but it is not suitable for low-cost sensor nodes. On the other hand, other localization schemes use only the connectivity information of sensor nodes so that localization is not much accurate enough. In this paper, we propose a range-free localization scheme, called Low-cost Two-hop Anchor Node-based Distributed Range-free Localization (LADL), which offers a higher accuracy with lower cost than the previous works. LADL exploits a small portion of anchor nodes which know their own location beforehand. In LADL, sensor nodes collect the location information of the anchor nodes within two-hop distance and calculate their own location using a grid-scan algorithm. The simulation results show that LADL has a maximum of 12% lower delivery cost of location information messages and 25% higher accuracy than DRLS [8].

Managing Frequent Updates in R-Trees for Update-Intensive Applications

Managing frequent updates is greatly important in many update-intensive applications, such as location-aware services, sensor networks, and stream databases. In this paper, we present an R-tree-based index structure (called Rsb-tree, R-tree with semibulk loading) for efficiently managing frequent updates from massive moving objects. The concept of semibulk loading is exploiting a small in-memory buffer to defer, buffer, and group the incoming updates and bulk-insert these updates simultaneously. With a reasonable memory overhead (typically only 1 percent of the whole data set), the proposed approach far outperforms the previous works in terms of update and query performance as well in a realistic environment. In order to further increase buffer hit ratio for the proposed approach, a new page-replacement policy that exploits the level of buffered node is proposed. Furthermore, we introduce the concept of deferring threshold ratio (dtr) that simply enables deferring CPU- and I/O-intensive operations such as node splits and removals. Extensive experimental evaluation reveals that the proposed approach is far more efficient than previous approaches for managing frequent updates under various settings.

Evaluation of Content-Matched Range Monitoring Queries over Moving Objects in Mobile Computing Environments

A content-matched (CM) range monitoring query over moving objects continually retrieves the moving objects (i) whose non-spatial attribute values are matched to given non-spatial query values; and (ii) that are currently located within a given spatial query range. In this paper, we propose a new query indexing structure, called the group-aware query region tree (GQR-tree) for efficient evaluation of CM range monitoring queries. The primary role of the GQR-tree is to help the server leverage the computational capabilities of moving objects in order to improve the system performance in terms of the wireless communication cost and server workload. Through a series of comprehensive simulations, we verify the superiority of the GQR-tree method over the existing methods.

Context-Enriched and Location-Aware Services

1Mobile Communications Division, Samsung Electronics Co., Ltd., 416 Maetan-dong, Youngtong-gu, Suwon, Gyeonggi-do 443-742, Republic of Korea 2Knowledge Engineering & Decision Support Research Group (GECAD), Institute of Engineering-Polytechnic of Porto (ISEP/IPP), Rua Dr. Antonio Bernardino de Almeida 431, 4200-072 Porto, Portugal 3Department of Computer Science, College of Engineering, Yonsei University, 134 Shinchon-dong, Seodaemoon-gu, Seoul 120-749, Republic of Korea

Towards Location-Based Real-Time Monitoring Systems in u-LBS

Recently, ubiquitous location-based services (u-LBS) has been utilized in a variety of practical and mission-critical applications such as security services, personalization services, location-based entertainment, and location-based commerce. The essence of u-LBS is actually the concept of location-awareness where location-aware devices perform more intelligent services for users by utilizing their locations. In order to realize/achieve this concept, a mobile device should continuously monitor the real-time contextual changes of a user; this is what we call location-based monitoring . In this paper, we discuss the research and technical issues on designing location-based real-time monitoring systems in u-LBS along with three major subjects: (1) high-performance spatial index, (2) monitoring query processing engine, and (3) distributed monitoring system with dynamic load balancing capability and energy-efficient management.