• KIPS Transactions on Computer and Communication Systems
• /
• v.5 no.9
• /
• pp.217-222
• /
• 2016

Recent research on integrated and peer-to-peer databases has produced new methods for handling various types of shared-group and process data. This paper with data publishing, where the publisher needs to specify certain sensitive information that should be protected. The proposed method cannot infer the user's sensitive information is leaked by XML constraints. In addition, the proposed secure framework uses encrypt to prevent the leakage of sensitive information from authorized users. In this framework, each node of sensitive data in an eXtensible Markup Language (XML) document is encrypted separately. All of the encrypted data are moved from their original document, and are bundled with an encrypted structure index. Our experiments show that the proposed framework prevents information being leaked via data inference.

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

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.27 no.2A
• /
• pp.116-123
• /
• 2002

The jacket matrix is based on the Walsh-Hadamard matrix and an extension of it. While elements of the Walsh-Hadamard matrix are +1, or -1, those of the Jacket matrix are ${\pm}$1 and ${\pm}$$\omega$, which is $\omega$, which is ${\pm}$j and ${\pm}$2$\sub$n/. This matrix has weights in the center part of the matrix and its size is 1/4 of Hadamard matrix, and it has also two parts, sigh and weight. In this paper, instead of the conventional Jacket matrix where the weight is imposed by force, a simple Jacket sequence generation method is proposed. The Jacket sequence is generated by AND and Exclusive-OR operations between the binary indices bits of row and those of column. The weight is imposed on the element by when the product of each Exclusive-OR operations of significant upper two binary index bits of a row and column is 1. Each part of the Jacket matrix can be represented by jacket sequence using row and column binary index bits. Using Distributed Arithmetic (DA), we present a VLSI architecture of the Fast Jacket transform is presented. The Jacket matrix is able to be applied to cryptography, the information theory and complex spreading jacket QPSK modulation for WCDMA.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2005.11a
• /
• pp.19-22
• /
• 2005

본 논문에서는 대용량 네트워크 트래픽 데이터를 대상으로 사이트의 프라이버시를 보호하면서 마이닝 결과의 정확성, 실용성 등을 보장할 수 있는 효율적인 순차 패턴 마이닝 기법을 제안한다. 네트워크가 발달함에 따라 네트워크 트래픽 데이터에 대한 마이닝은 네트워크를 통한 통신의 패턴을 찾아내고, 이를 사용하여 침입 탐지, 인터넷 웜의 탐지 등으로 유용하게 쓰이게 되었다. 그러나 네트워크 트래픽 데이터는 네트워크 사용자 개개인의 인터넷 접속 형태, IP 주소 등의 정보를 포함하는 데이터로 네트워크 사용자의 프라이버시를 해칠 수 있다는 문제점이 존재한다. 따라서 이들 네트워크 트래픽 데이터를 대상으로 하는 마이닝 기법에서는 프라이버시 보호를 위하여 각 사이트에 저장되어 있는 네트워크 트래픽 데이터를 공개하지 않으면서도, 의미있는 패턴을 찾을 수 있어야 한다. 본 논문에서는 프라이버시 보호를 위하여 N-저장소 서버 모델을 제안한다. 제안된 모델에서는 데이터를 분할하여 암호화한 후, 이를 복호화할 수 없는 서버에서 집계하는 방식을 사용하여 실제 데이터가 저장되어 있는 각 사이트의 출처 정보를 감추는 방식을 사용한다. 또한, 효율적인 빈번 패턴 생성을 위하여 빈번 항목에 대한 인덱스 구조를 제안하고, 이를 기반으로 한 순차 패턴 마이닝 기법을 보인다.

• Journal of Convergence for Information Technology
• /
• v.10 no.11
• /
• pp.16-22
• /
• 2020

Since it is difficult for individual power producers and non-professionals who lack basic knowledge to control, manage, and operate facilities through EMS (Energy Management System), augmented reality and virtual reality monitoring systems are applied. However, in the existing systems, data access efficiency is inferior due to the process of analyzing column values for analog signal values collected from sensors and converting data after combining the columns. In addition, high-speed operation processing is difficult due to a large number of indexes for accommodating access patterns for various analog signal waveforms. Therefore, in this paper, a bitmap generator is inserted into a non-tree structure to convert the data collected from power facilities into physical property information, and the converted information is encrypted with a common key, so that the resources for the resources shared between each device are We propose a method of exchanging data for an augmented reality-based power system that is controlled by measurement.