Min Yoon

A Signature-Based Data Security Technique for Energy-Efficient Data Aggregation in Wireless Sensor Networks

Data aggregation techniques have been widely used in wireless sensor networks (WSNs) to solve the energy constraint problems of sensor nodes. They can conserve the significant amount of energy by reducing data packet transmission costs. However, many data aggregation applications require privacy and integrity protection of the real data while transmitting data from the sensing nodes to a sink node. The existing schemes for supporting both privacy and integrity, that is, iCDPA, and iPDA, suffer from high communication cost, high computation cost, and data propagation delay. To resolve the problems, we propose a signature-based data security technique for protecting sensitive data aggregation in WSNs. To support privacy-preserving data aggregation and integrity checking, our technique makes use of the additive property of complex numbers. Out of two parts of a complex number, the real part is used to hide the sampled data of a sensor node from its neighboring nodes and adversaries, whereas the imaginary part is used for data integrity checking at both data aggregators and the sink node. Through a performance analysis, we prove that our privacy-preserving data aggregation scheme outperforms the existing schemes up to 50% in terms of communication and computation overheads as well as up to 3 times in terms of integrity checking and data propagation delay.

A privacy-aware query authentication index for database outsourcing

Recently, cloud computing has been spotlighted as a new paradigm of database management system. In this environment, databases are outsourced and deployed on a service provider in order to reduce cost for data storage and maintenance. However, the service provider might be untrusted so that the two issues of data security, including data confidentiality and query result integrity, become major concerns for users. Existing bucket-based data authentication methods have problem that the original spatial data distribution can be disclosed from data authentication index due to the unsophisticated data grouping strategies. In addition, the transmission overhead of verification object is high. In this paper, we propose a privacy-aware query authentication which guarantees data confidentiality and query result integrity for users. A periodic function-based data grouping scheme is designed to privately partition a spatial database into small groups for generating a signature of each group. The group signature is used to check the correctness and completeness of outsourced data when answering a range query to users. Through performance evaluation, it is shown that proposed method outperforms the existing method in terms of range query processing time up to 3 times.

Design and Implementation of Cluster-Based Routing Protocol Using Message Success Rate in Sensor Networks

Recently, Wireless Sensor Network (WSN) has been broadly studied in ubiquitous computing environments. In WSN, it is important to reduce communication overhead by using an energy-efficient routing protocol because the resources of the sensor node are limited. Although there exist some cluster-based routing protocols, they have some problems. First, the random selection of a cluster head incurs a node concentration problem. Secondly, they have a low reliability for data communication due to the less consideration of node communication range. Finally, data communication overhead is greatly increased while constructing clusters. To solve these problems, we, in this paper, propose a new cluster-based routing protocol using message success rate. To resolve the node concentration problem, we design a new cluster head selection algorithm based on node connectivity and devise cluster maintenance algorithms. Moreover, to guarantee data communication reliability, we use message success rate, which is one of popular measures for data communication reliability, in order to select a routing path. Finally, to reduce data communication overhead, we use only the information of neighboring nodes during both cluster construction and cluster head selection phases. Through our performance analysis, we show that our protocol outperforms existing schemes in terms of communication reliability and energy efficiency.

A New Spatial Transformation Scheme for Preventing Location Data Disclosure in Cloud Computing

Because much interest in spatial database for cloud computing has been attracted, studies on preserving location data privacy have been actively done. However, since the existing spatial transformation schemes are weak to a proximity attack, they cannot preserve the privacy of users who enjoy location-based services in the cloud computing. Therefore, a transformation scheme is required for providing a safe service to users. We, in this paper, propose a new transformation scheme based on a line symmetric transformation (LST). The proposed scheme performs both LST-based data distribution and error injection transformation for preventing a proximity attack effectively. Finally, we show from our performance analysis that the proposed scheme greatly reduces the success rate of the proximity attack while performing the spatial transformation in an efficient way.

A new cloaking algorithm using Hilbert curves for privacy protection

Due to the advancement of GPS facilitates, the use of Location Based Service (LBS) has recently been increased rapidly. Since LBS needs the location of user, the private and confidential information of user may disclose to others. To protect the privacy of users, many cloaking algorithms have been proposed to hide users actual location. The existing Hilbert cloaking algorithm shows its high accuracy in terms of location privacy, but it has a drawback that it extends a cloaking region inefficiently due to the dimensionality reduction. In this paper, we propose a new cloaking algorithm which can avoid the unnecessary extension of cloaking region. Our algorithm optimizes the generation of a cloaking region by storing adjacent cell information being not connected by Hilbert curve. From experimental results, it is shown that our proposed cloaking algorithm outperforms the existing Hilbert algorithm.

Intelligent Context-Aware System Architecture in Pervasive Computing Environment

In this paper, we design an intelligent system architecture for dealing with context-aware application services in pervasive computing environment. The context-aware intelligent system architecture is composed of middleware, context server, and client. The middleware component of our intelligent system architecture plays an important role in recognizing a moving node with mobility by using a Bluetooth wireless communication technology as well as in executing an appropriate execution module according to the context acquired from a context server. The context server functions as a manager that efficiently stores into the database server context information, such as users current status, physical environment, and resources of a computing system. To verify the usefulness of our context-aware intelligent system architecture, we finally develop a context-aware application to provide users with a music playing service in pervasive computing environment.

Performance Analysis of MapReduce-Based Distributed Systems for Iterative Data Processing Applications

Recently, research on big data has been actively made because big data are generated in various scientific applications, such as biology and astronomy. Therefore, distributed data processing techniques have been studied to manage the big data in large number servers. Meanwhile, some scientific applications like genome data analysis require loop control in analyzing big data using a MapReduce framework. In this paper, we first describe the existing MapReduce-based distributed systems which support iterative data processing. In addition, we do the performance analysis of the existing distributed systems in terms of execution time for various scientific applications which require iterative data processing. Finally, based on the performance analysis, we discuss some requirements for a new MapReduce-based distributed system which supports iterative data processing efficiently.

A New Grid-Based Cloaking Scheme for Continuous Queries in Centralized LBS Systems

Recent development in wireless communication technology and mobile equipment is making location-based services (LBSs) more popular day by day. However, because users continuously send queries to a server by using their exact locations in the LBSs, private information can be in danger. Therefore, a mechanism for users’ privacy protection is required for the safe and comfortable use of LBSs. For this, we, in this paper, propose a grid-based cloaking area creation scheme in order to support continuous queries in LBSs. Our scheme creates a cloaking area rapidly by using grid-based cell expansion to efficiently support the continuous LBSs. In addition, to generate a cloaking area which lowers the exposure probability of a mobile user to a minimum level, our scheme computes a privacy protection degree by granting weights to the mobile users. Finally, we show from our performance analysis that our cloaking scheme shows better performance than the existing cloaking scheme.

A New Middleware for Distributed Data Processing in CUBRID DBMS

Abstract With the sharply increasing amount of data, studies on bigdata processing based on NoSQL have been actively done. However, NoSQL cannot satisfy the ACID properties of database transactions. Therefore, bigdata processing based on RDBMS has been spotlighted. CUBRID Shard stores data in the distributed CUBRID servers by dividing the database. However, CUBRID Shard cannot process a query when data of a user is distributed on the multiple CUBRID servers. Therefore, in this paper we propose a CUBRID based middleware which supports distributed data processing. Through the performance evaluations, we show that our proposed scheme shows better performance than the existing work in terms of query processing time.

A New Query Integrity Verification Method for Encrypted Data in Database Outsourcing

In database outsourcing, two issues of data security emerge: data confidentiality and data integrity. Existing data transformation schemes were widely studied for preserving data confidentiality, but they are vulnerable to data leakage problem because they do not consider data distribution when encrypting data. Meanwhile, query authentication schemes verifying data integrity, suffer from transmission overhead for verification data. In this paper, we propose a privacy-aware query authentication scheme which guarantees the data confidentiality and the query result integrity of sensitive data. To solve the original data leakage problem, we propose a bitmap-based data transformation scheme with anchor selection based on data distribution. Also, we devise a query result authentication index that stores an encrypted signature for each anchor so that it can reduce the amount of auditing data. Through performance evaluation, we show that our scheme outperforms the state-of-the-art method in terms of query processing time and verification overhead.