Hyeong-Il Kim

Hilbert curve-based cryptographic transformation scheme for spatial query processing on outsourced private data

Research on preserving location data privacy in outsourced databases has been spotlighted with the development of cloud computing. However, the existing spatial transformation schemes are vulnerable to various attack models. The existing cryptographic transformation scheme provides good data privacy, but it has a high query processing cost. To improve privacy and reduce cost, we propose a Hilbert curve-based cryptographic transformation scheme to preserve the privacy of the spatial data from various attacks on outsourced databases. We also provide efficient range and k-NN query processing algorithms using a Hilbert-order index. A performance analysis confirms that the proposed scheme is robust against attack models and achieves better query processing performance than the existing cryptographic transformation scheme.

Hilbert-curve based cryptographic transformation scheme for protecting data privacy on outsourced private spatial data

The study on spatial database outsourcing has been spotlighted with the development of cloud computing. Therefore, researches for protecting location data privacy in outsourced database have been studied. However, the existing spatial transformation schemes are vulnerable to naïve attack models. The existing cryptographic transformation scheme provides high data privacy, but it causes the high query processing cost. To solve these problems, in this paper we propose a Hilbert-curve based cryptographic transformation scheme to protect data privacy and to improve the efficiency of the query processing in outsourced databases. The proposed scheme reduces the communication cost for query processing since we perform a local clustering based on the order of Hilbert-curve. Finally, we show from performance analysis that the proposed scheme outperforms the existing scheme.

k -Nearest Neighbor Query Processing Algorithms for a Query Region in Road Networks

Recent development of wireless communication technologies and the popularity of smart phones are making location-based services (LBS) popular. However, requesting queries to LBS servers with users’ exact locations may threat the privacy of users. Therefore, there have been many researches on generating a cloaked query region for user privacy protection. Consequently, an efficient query processing algorithm for a query region is required. So, in this paper, we propose k-nearest neighbor query (k-NN) processing algorithms for a query region in road networks. To efficiently retrieve k-NN points of interest (POIs), we make use of the Island index. We also propose a method that generates an adaptive Island index to improve the query processing performance and storage usage. Finally, we show by our performance analysis that our k-NN query processing algorithms outperform the existing k-Range Nearest Neighbor (kRNN) algorithm in terms of network expansion cost and query processing time.

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 New Grid-Based Cloaking Algorithm for Privacy Protection in Location-Based Services

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. For this, a mechanism to deal with the users’ privacy protection is required. In this paper, we propose a new cloaking algorithm for privacy protection in LBSs. Our cloaking algorithm can support both k-anonymity and l-diversity. That is, it first creates a minimum cloaking region by finding l buildings (l-diversity) and then finds k users (k-anonymity). To generate the minimum cloaking region efficiently, we make use of a grid structure for storing buildings and users as well as a pruning technique for reducing unnecessary computation. Finally, we show from our performance analysis that our cloaking algorithm outperforms the existing grid-based cloaking algorithm, in terms of the size of cloaking regions, their creation time and query processing time based on them.

A kNN query processing algorithm using a tree index structure on the encrypted database

With the adoption of cloud computing, database outsourcing has emerged as a new platform. Due to the serious privacy concerns in the cloud, database need to be encrypted before being outsourced to the cloud. Therefore, various kNN query processing techniques have been proposed over the encrypted database. However, the existing schemes are either insecure or inefficient. So, we, in this paper, propose a new secure kNN query processing algorithm. Our algorithm guarantees the confidentiality of both the encrypted data and a users query record. To achieve the high query processing efficiency, we also devise an encrypted index search scheme which can perform data filtering without revealing data access patterns. We show from our performance analysis that the proposed scheme outperforms the existing scheme in terms of a query processing cost while preserving data privacy.

A Privacy-Preserving Top-k Query Processing Algorithm in the Cloud Computing

Cloud computing has emerged as a new platform for storing and managing databases. As a result, a database outsourcing paradigm has gained much interests. To prevent the contents of outsourced databases from being revealed to cloud computing, databases must be encrypted before being outsourced to the cloud. Therefore, various Top-k query processing techniques have been proposed for encrypted databases. However, there is no existing work that can not only hide data access patterns, but also preserve the privacy of user query. To solve the problems, in this paper, we propose a new privacy-preserving Top-k query processing algorithm. Our algorithm protects the user query from the cloud and conceals data access patterns during query processing. A performance analysis shows that the proposed scheme provide good scalability without any information leakage.

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 Cloaking Scheme for Continuous Location-Based Services in Distributed Systems

Recently, many people are using location-based services (LBSs). However, since users continuously request LBS queries to LBS server by sending their exact location data, their privacy information is in danger. Therefore, cloaking schemes have been proposed to protect the user’s location privacy. However, because the existing techniques generate a cloaking area in a centralized manner, an anonymizer suffers from performance degradation and security problem. Therefore, a cloaking scheme based on distributed system is required for the safe and comfortable use of LBSs by mobile users. In this paper, we propose a grid-based cloaking scheme for continuous location-based services in distributed systems. The proposed scheme stores information and performs operations in a distributed manner to create a cloaking area. Finally, we show from a performance analysis that our cloaking scheme shows better performance than the existing centralized cloaking schemes.

A spatial transformation scheme supporting data privacy and query integrity for security of outsourced databases

Outsourcing database to a third-party data provider is becoming a common practice for data owners to avoid the cost of managing and maintaining the database. Meanwhile, because of the popularity of location-based services, the need for spatial data is increasing dramatically. However, the most important challenge in database outsourcing is how to meet privacy requirements and guarantee the integrity of the query result as well. Unfortunately, most of the existing techniques support either data privacy or integrity on spatial databases. To carry on both privacy and integrity for outsourced spatial data, we propose a spatial transformation scheme that makes use of shearing transformation with rotation shifting. We describe attack models measuring the data privacy of our transformation scheme. Finally, our extensive experiments have demonstrated that our scheme has adequate strength for data privacy by showing outstanding performance against different kinds of attack models and efficiently handles the query integrity of the query result sets. Copyright