Young-Sung Shin

K-Anonymous Cloaking Algorithm Based on Weighted Adjacency Graph for Preserving Location Privacy

The propagation of position identifying devices, such as GPS (Global Positioning System), becomes increasingly a privacy threat in location-based services (LBSs). However, in order to enjoy such services, the user must precisely disclose his/her exact location to the LBS. So, it is a key challenge to efficiently preserve users privacy while accessing LBS. For this, the existing method employs a 2PASS cloaking framework that not only hides the actual user location but also reduces bandwidth consumption. However, it suffers from privacy attack. Therefore, we aim to provide the solutions which can preserve user privacy by utilizing k-anonymity mechanism. In this paper, we propose a weighted adjacency graph based k-anonymous cloaking technique that can provide protection to user and also reduce bandwidth usages. Our cloaking approach efficiently supports k-nearest neighbor queries without revealing private information of the query initiator. We demonstrate via experimental results that our algorithm yields much better performance than the existing one.

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.

Efficient and secure top-k query processing algorithm using garbled circuit based secure protocols on outsourced databases

With the growth of cloud computing, database outsourcing has attracted much interests. Due to the serious privacy threats in cloud computing, databases needs to be encrypted before being outsourced to the cloud. Therefore, various Top-k query processing algorithms have been studied for encrypted databases. However, existing algorithms are either insecure or inefficient. Therefore, in this paper we propose an efficient and secure Top-k query processing algorithm. Our algorithm guarantees the confidentiality of both the data and a user query while hiding data access patterns. Our algorithm also enables the query issuer not to participate in the query processing. To achieve a high level of query processing efficiency, we use new secure protocols using Yaos garbled circuit and a data packing technique. A performance analysis shows that the proposed algorithm outperforms the existing works in terms of query processing costs.

A Content-Aware Expert Recommendation Scheme in Social Network Services

Because a wide range of professionals utilize Social Network Service (SNS), the SNS users have recently required an expert recommendation service to enable users to perform both cooperation and technical communication with experts. A content-boosted collaborative filtering (CBCF) provides various prediction algorithms which support effective recommendations. However, the CBCF cannot calculates the similarity of items (or users) when the calculation condition is not clearly provided. To solve the problem, we propose a content-aware hybrid collaborative filtering scheme for expert recommendation in SNSs. Finally, we show from a performance analysis that our scheme outperforms the existing method in terms of recommendation accuracy.

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.

A Middleware Supporting Query Processing on Distributed CUBRID

Due to the shortages of NoSQL, studies on RDBMS based bigdata processing have been actively performed. Although they can store data in the distributed servers by dividing the database, they cannot process a query when data of a user is distributed on the multiple servers. Therefore, in this paper we propose a CUBRID based middleware supporting distributed parallel query processing. Through the performance evaluations, we show that our proposed scheme outperforms the existing work in terms of query processing time.

A Semi-clustering Scheme for Large-Scale Graph Analysis on Hadoop

With the evolution of IT technologies, large-scale graph data have lately become a growing interest. As a result, there are a lot of research results in large-scale graph analysis on Hadoop. The graph analysis based on Hadoop provides parallel programming models with data partitioning and contains iterative phases of MapReduce jobs. Therefore, the effectiveness of data partitioning depends on how the data partitioning maintains data locality in each node of cluster. In this paper, we propose a semi-clustering scheme for large-scale graph analysis such as PageRank algorithm on Hadoop and show that the proposed scheme is effective. With experiment results, PageRank computation with the semi-clustering improves the performance.

Scalability in Privacy-Preserving Data Aggregation for Wireless Sensor Networks

In this paper, we propose a new privacy preserving data aggregation scheme for WSNs. Our scheme applies additive property of complex numbers in order to combine sensor data and preserve data privacy during transmission to the sink node. In addition, for supporting scalability, we propose a novel mechanism in which a special set of real numbers are assigned to sensor nodes as their IDs so that a single bit is sufficient to hold ID of a sensor node during transmission of aggregated data to the sink node. For this, we, first, generate fixed size signatures for the IDs of all sensor nodes and then superimpose the signatures during data aggregation phase. By analytical evaluations, we show that our scheme is more scalable and energy efficient than the existing methods to achieve data privacy and transmit IDs of sensor nodes along with the aggregated data to the sink node.