Jae-Woo Chang

A Secure Range Query Processing Algorithm for the Encrypted Database on the Cloud

Secure range query processing algorithms have been studied as the range query can be used as a baseline technique in various fields. However, when processing a range query, the existing methods fail to hide the data access patterns which can be used to derive the actual data items and the private information of a querying issuer. The problem is that the data access patterns can be exposed even though the data and query are encrypted. So, in this paper we propose a new range query processing algorithm on the encrypted database. Our method conceals the data access patterns while supporting efficient query processing by using our proposed encrypted index search scheme. Through the performance analysis, we show that the proposed range query processing algorithm can efficiently process a query while hiding the data access patterns.

Secure In-memory Key-Value Storage with SGX.

Motivation: Recently, HW-based trusted execution environments such as Intel Software Guard Extension (SGX) can provides secure isolated execution environment on remote systems under the untrusted privileged system software [2]. However, Intel SGX has the limitation of the capacity of the protected memory region (∼96MB, called enclave page cache (EPC)), and more demands for the protected memory beyond the limit cause costly page re-mappings. Although one of the most important applications benefiting from the enhanced security from SGX is the in-memory key-value storage, its memory requirements are far larger than the memory limit. Furthermore, the main data structures commonly use finegrained data items such as pointers and keys, which do not match well with the coarse-grained page re-mapping of the enclave memory extension. To overcome such limitations, this paper proposes a new secure in-memory key-value storage designed for the shielded execution environments. Our key-value storage provides the application-specific data protection, which maintains the main data structures on the unprotected memory region with finegrained encryption and integrity protection on each key-value pair. Architecture: To mitigate the enclave memory restriction, the proposed key-value storage stores the main data structure, a hash index structure with chaining in this paper, in the unprotected memory region, only the main secret key and integrity meta-data are stored in the EPC region. When an access to a key occurs, the corresponding key-value pair is searched from the unprotected memory region. As the main data structure is not protected by the SGX hardware, each data entry must be encrypted by the process in the enclave, and updated to the main hash table for set operations. For a get operation, the encrypted data entry is read from the main hash table in non-enclave pages. The data entry is decrypted and its integrity is verified by comparing message authentication code (MAC). In other words, instead of relying on the

Matrix-based Filtering and Load-balancing Algorithm for Efficient Similarity Join Query Processing in Distributed Computing Environment

하둡 맵리듀스와 같은 분산 컴퓨팅 플랫폼이 개발됨에 따라, 기존 단일 컴퓨터 상에서 수행되는 질의 처 리 기법을 분산 컴퓨팅 환경에서 효율적으로 수행하는 것이 필요하다. 특히, 주어진 두 데이터 집합에서 유사도가 높은 모든 데이터 쌍을 탐색하는 유사 조인 질의를 분산 컴퓨팅 환경에서 수행하려는 연구가 있어 왔다. 그러나 분산 병렬 환경에서의 기존 유사 조인 질의처리 기법은 데이터 전송 비용만을 고려하기 때문 에 클러스터 간에 비균등 연산 부하 분산의 문제점이 존재한다. 본 논문에서는 분산 컴퓨팅 환경에서 효율 적인 유사 조인 처리를 위한 행렬 기반 부하 분산 알고리즘을 제안한다. 제안하는 알고리즘은 클러스터의 균등 부하 분산을 위해 행렬을 이용하여 예상되는 연산 부하를 측정하고 이에 따라 파티션을 생성한다. 아 울러, 클러스터에서 질의 처리에 사용되지 않는 데이터를 필터링함으로서 연산 부하를 감소시킨다. 마지막 으로 성능 평가를 통해 제안하는 알고리즘이 기존 기법에 비해 질의 처리 성능 측면에서 우수함을 보인다.

Design and Implementation of HDFS Data Encryption Scheme Using ARIA Algorithms on Hadoop

요 약 최근 스마트폰 기기의 보급 및 소셜 서비스 산업의 고도화로 인해, 빅데이터가 등장하였다. 한편 빅 데이터에서 효율적으로 정보를 분석하는 대표적인 플랫폼으로 하둡이 존재한다. 하둡은 클러스터 환경 에 기반한 우수한 확장성, 장애 복구 기능 및 사용자가 기능을 정의할 수 있는 맵리듀스 프레임워크 등을 지원한다. 아울러 하둡은 개인정보나 위치 데이터 등의 민감한 정보를 보호하기 위해 Kerberos를 통한 사용자 인증 기법을 제공하고, HDFS 압축 코덱을 활용한 AES 코덱 기반 데이터 암호화를 지원 하고 있다. 그러나 하둡 기반 소프트웨어를 사용하고 있는 국내 기관 및 기업은 국내 ARIA 데이터 암 호화를 적용하지 못하고 있다. 이를 해결하기 위해 본 논문에서는 하둡을 기반으로 ARIA 암호화를 지 원하는 HDFS 데이터 암호화 기법을 제안한다.

Efficient Top-k Query Processing Algorithm Using Grid Index-based View Selection Method

Research on top-k query processing algorithms for analyzing big data have been spotlighted recently. However, because existing top-k query processing algorithms do not provide an efficient index structure, they incur high query processing costs and cannot support various types of queries. To solve these problems, we propose a top-k query processing algorithm using a view selection method based on a grid index. The proposed algorithm reduces the query processing time by retrieving the minimum number of grid cells for the query range, by using a grid index-based view selection method. Finally, we show from our performance analysis that the proposed scheme outperforms an existing scheme, in terms of both query processing time and query result accuracy.

A MapReduce-based kNN Join Query Processing Algorithm for Analyzing Large-scale Data

Recently, the amount of data is rapidly increasing with the popularity of the SNS and the development of mobile technology. So, it has been actively studied for the effective data analysis schemes of the large amounts of data. One of the typical schemes is a Voronoi diagram based on kNN join algorithm (VkNN-join) using MapReduce. For two datasets R and S, VkNN-join can reduce the time of the join query processing involving big data because it selects the corresponding subset Sj for each Ri and processes the query with them. However, VkNN-join requires a high computational cost for constructing the Voronoi diagram. Moreover, the computational overhead of the VkNN-join is high because the number of the candidate cells increases as the value of the k increases. In order to solve these problems, we propose a MapReduce-based kNN-join query processing algorithm for analyzing the large amounts of data. Using the seed-based dynamic partitioning, our algorithm can reduce the overhead for constructing the index structure. Also, it can reduce the computational overhead to find the candidate partitions by selecting corresponding partitions with the average distance between two seeds. We show that our algorithm has better performance than the existing scheme in terms of the query processing time.

A Query Result Integrity Assurance Scheme Using an Order-preserving Encryption Scheme in the Database Outsourcing Environment

Recently, research on database encryption for data protection and query result authentication methods has been performed more actively in the database outsourcing environment. Existing database encryption schemes are vulnerable to order matching and counting attack of intruders who have background knowledge of the original database domain. Existing query result integrity auditing methods suffer from the transmission overhead of verification object. To resolve these problems, we propose a group-order preserving encryption index and a query result authentication method based on the encryption index. Our group-order preserving encryption index groups the original data for data encryption and support query processing without data decryption. We generate group ids by using the Hilbert-curve so that we can protect the group information while processing a query. Finally, our periodic function based data grouping and query result authentication scheme can reduce the data size of the query result verification. Through performance evaluation, we show that our method achieves better performance than an existing bucket-based verification scheme, it is 1.6 times faster in terms of query processing time and produces verification data that is 20 times smaller.

Grid-based Index Generation and k-nearest-neighbor Join Query-processing Algorithm using MapReduce

MapReduce provides high levels of system scalability and fault tolerance for large-size data processing. A MapReduce-based k-nearest-neighbor(k-NN) join algorithm seeks to produce the k nearest-neighbors of each point of a dataset from another dataset. The algorithm has been considered important in bigdata analysis. However, the existing k-NN join query-processing algorithm suffers from a high index-construction cost that makes it unsuitable for the processing of bigdata. To solve the corresponding problems, we propose a new grid-based, k-NN join query-processing algorithm. Our algorithm retrieves only the neighboring data from a query cell and sends them to each MapReduce task, making it possible to improve the overhead data transmission and computation. Our performance analysis shows that our algorithm outperforms the existing scheme by up to seven-fold in terms of the query-processing time, while also achieving high extent of query-result accuracy.