• Journal of KIISE
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• v.44 no.7
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• pp.703-709
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• 2017

Outsourcing databases is to offload storage and computationally intensive tasks to the third party server. Therefore, data owners can manage big data, and handle queries from clients, without building a costly infrastructure. However, because of the insecurity of network systems, the third-party server may be untrusted, thus the query results from the server may be tampered with. This problem has motivated significant research efforts on authenticating various queries such as range query, kNN query, function query, etc. Although aggregation queries play a key role in analyzing big data, authenticating aggregation queries has not been extensively studied, and the previous works are not efficient for data with high dimension or a large number of distinct values. In this paper, we propose the AMR-tree that is a data structure, applied to authenticate aggregation queries. We also propose an efficient proof construction method and a verification method with the AMR-tree. Furthermore, we validate the performance of the proposed algorithm by conducting various experiments through changing parameters such as the number of distinct values, the number of records, and the dimension of data.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1087-1101
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• 2018

As cloud computing has become a widespread technology, malicious attackers can obtain the private information of users that has leaked from the service provider in the outsourced databases. To resolve the problem, it is necessary to encrypt the database prior to outsourcing it to the service provider. However, the most existing data encryption schemes cannot process a query without decrypting the encrypted databases. Moreover, because the amount of the data is large, it takes too much time to decrypt all the data. For this, Programmable Order-Preserving Secure Index Scheme (POPIS) was proposed to hide the original data while performing query processing without decryption. However, POPIS is weak to both order matching attacks and data count attacks. To overcome the limitations, we propose a group order-preserving data encryption scheme (GOPES) that can support efficient query processing over the encrypted data. Since GOPES can preserve the order of each data group by generating the signatures of the encrypted data, it can provide a high degree of data privacy protection. Finally, it is shown that GOPES is better than the existing POPIS, with respect to both order matching attacks and data count attacks.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.562-566
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• 2016

클라우드 컴퓨팅의 발전과 더불어 데이터베이스 아웃소싱에 대한 연구가 활발히 진행되고 있다. 그러나 기존 정보보호를 지원하는 Topk 질의처리 연구는 다양한 형태로 정보가 노출되는 문제점을 보인다. 따라서 본 논문에서는 데이터 보호, 사용자 질의 보호, 데이터 접근 패턴 은닉을 모두 지원하는 아웃소싱 데이터베이스 상에서의 안전한 Topk 질의처리 알고리즘을 제안한다. 성능평가를 통해, 제안하는 기법이 정보보호를 지원하는 동시에 효율적인 성능을 제공함을 보인다.

• Journal of KIISE
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• v.43 no.12
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• pp.1437-1457
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• 2016

In outsourced databases, the cloud provides an authorized user with querying services on the outsourced database. However, sensitive data, such as financial or medical records, should be encrypted before being outsourced to the cloud. Meanwhile, k-Nearest Neighbor (kNN) query is the typical query type which is widely used in many fields and the result of the kNN query is closely related to the interest and preference of the user. Therefore, studies on secure kNN query processing algorithms that preserve both the data privacy and the query privacy have been proposed. However, existing algorithms either suffer from high computation cost or leak data access patterns because retrieved index nodes and query results are disclosed. To solve these problems, in this paper we propose a new kNN query processing algorithm on the encrypted database. Our algorithm preserves both data privacy and query privacy. It also hides data access patterns while supporting efficient query processing. To achieve this, we devise an encrypted index search scheme which can perform data filtering without revealing data access patterns. Through the performance analysis, we verify that our proposed algorithm shows better performance than the existing algorithms in terms of query processing times.

• Journal of the Korean Society for Library and Information Science
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• v.38 no.2
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• pp.119-136
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• 2004

The purpose of this study is to analyze cases of several academic libraries, both in Korea and other countries, which have outsourced part or all of cataloging operations and examine current trends and controversy over outsourcing. Contrary to expectations of reduced expenses and increased efficiency in cataloging, outsourcing often results in bibliographic records of low quality which require additional costs to correct errors and maintain consistency and accuracy of records, and therefore, fails to reach the goal. Due to the absence of high quality bibliographic utilities, union catalogs, and authority databases, and lack of expertise in cataloging among outsourcing service providers, it seems that outsourcing is not currently an appropriate tactic for cataloging in academic libraries in Korea.

• Journal of KIISE
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• v.42 no.5
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• pp.652-663
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• 2015

Due to the popularity of location-based services, the amount of generated spatial data in daily life has been dramatically increasing. Therefore, spatial database outsourcing has become popular for data owners to reduce the spatial database management cost. The most important consideration in database outsourcing is meeting the privacy requirements and guarantying the integrity of the query result. However, most of existing database transformation techniques do not support both of the data privacy and integrity of the query result. To solve this problem, we propose a spatial data transformation scheme that utilizes the shearing transformation with rotation shifting. In addition, we described the attack models to measure the data privacy of database transformation schemes. Finally, we demonstrated through the experimental evaluations that our scheme provides high level of data protection against different kinds of attack models, compared to the existing schemes, while guaranteeing the integrity of the query result sets.