Miyoung Jang

A New Energy-Efficient Cluster-Based Routing Protocol Using a Representative Path in Wireless Sensor Networks

Wireless sensor networks (WSNs) have been broadly studied with advances in ubiquitous computing environment. Because the resource of a sensor node is limited, it is important to use energy-efficient routing protocol in WSNs. The cluster-based routing is an efficient way to reduce energy consumption by decreasing the number of transmitted messages to the sink node. LEACH is the most popular cluster-based routing protocol, which provides an adaptive cluster generation and cluster header rotation. However, its communication range is limited since it assumes a direct communication between sensor nodes and a sink node. To resolve this problem, we propose a new energy-efficient cluster-based routing protocol, which adopts a centralized clustering approach to select cluster headers by generating a representative path. To support reliable data communication, we propose a multihop routing protocol that allows both intra- and intercluster communications. Based on a message success rate and a representative path, the sensor nodes are uniformly distributed in clusters so that the lifetime of network can be prolonged. Through performance analysis, we show that our energy-efficient routing protocol outperforms the existing protocols up to 2 times, in terms of the distribution of cluster members, the energy consumption, and the reliability of a sensor network.

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.

A New Cloaking Method Supporting both K-anonymity and L-diversity for Privacy Protection in Location-Based Service

In Location-Based Services (LBSs), users send location-based queries to LBS servers along with their exact locations, but the location information of the users can be misused by adversaries. In this regard, there must be a mechanism which can deal with the privacy protection of the users. In this paper, we propose a cloaking method considering both K-anonymity and L-diversity. Our cloaking method creates a minimum cloaking region by finding L number of buildings (L-diversity) and then finds K number of users (K-anonymity). To support it, we use R*-tree based index structures as well as efficient filtering techniques to generate a minimum cloaking region. Finally, we show from our performance analysis that our cloaking method outperforms the existing grid-based cloaking method in terms of the size of cloaking regions and cloaking region creation time.

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 Grid-Based k-Nearest Neighbor Join for Large Scale Datasets on MapReduce

Because MapReduce supports efficient parallel data processing, MapReduce-based query processing algorithms have been widely studied. Among various query types, k-nearest neighbor join, which aims to produce the k nearest neighbors of each point of a dataset from another dataset, has been considered most important in data analysis. Existing k-NN join query processing algorithms on MapReduce suffer from high index construction and computation costs which make them unsuitable for big data processing. In this paper, we propose a new grid-based k-NN join query processing algorithm on MapReduce. First, we design a dynamic grid index that represents the distribution of join datasets. Based on this index, we prune out unnecessary cells for the join with the distance-based filtering. This can reduce the data transmission and computation overheads. From performance analysis, we show that our algorithm outperforms the existing scheme up to seven times in terms of query processing time while achieving high query result accuracy.

Cache View Based Top-k Query Processing for Encrypted Data Analysis

With the development of cloud computing technology, database outsourcing has recently attracted much interest. However, because the users’ data may contain sensitive personal information, it is essential to encrypt the database to be outsourced for protecting users’ privacy. Meanwhile, cache-based Top-k query processing schemes were proposed to support efficient analysis of a large amount of data. However, the existing works have a problem that they cannot process a Top-k query on the encrypted data. To solve this problem, we propose a cache view-based Top-k query processing algorithm by using an order-preserving encrypted index. To improve the performance of the top-k query processing, we newly design a score function for calculating the similarity between a given query and the cached query. Finally, we show from the performance analysis that our scheme outperforms the existing work in terms of the query processing time and the query result accuracy.

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 Grid-Based Approximate K-NN Query Processing Algorithm for Privacy Protection in Location-Based Services

Location-Based Services (LBSs) are becoming popular due to the advances in wireless networks and positioning capabilities. Providing user’s exact location to the LBS server may lead revealing his private information to unauthorized parties (e.g., adversaries). There exist two main fields of research to overcome this problem. They are cloaking region based query processing methods which blur a user’s location into a cloaking region and Private Information Retrieval (PIR) based query processing methods which encrypt location data by using PIR protocol. However, the main disadvantages of existing work are high computation and communication overheads. To resolve these problems, we propose a grid-based approximate k-NN query processing algorithm by combining above two methods. Through performance analysis, we have shown that our scheme outperforms the existing work in terms of both query processing time and accuracy of the result set.

An Efficient Partition-Based Filtering for Similarity Joins on MapReduce Framework

Similarity join is an important operation in MapReduce framework to find pairs of similar objects like images, video and time series. Since MapReduce basics do not support efficient join processing, the duplicate reduction of candidates and load-balancing among partitions are the major challenges. Recently, many partition based similarity join algorithms have been proposed to solve such problems. However, the existing algorithms still have limitations for supporting efficient join processing over large-scale data set. In this paper, we proposed a similarity join algorithm with an efficient filtering technique on MapReduce to overcome the limitations of traditional partitioning method in two ways: (1) the number of outputs records generated by the filtering matrix reduces duplicates and (2) the estimated join cost generated by using a partition matrix leads to a better load-balance among reducers. Moreover, we have conducted experimental evaluations using sequential data to show the speed-up and scale-up of proposed method.