APHID: An architecture for private, high-performance integrated data mining

被引：5

作者：

Secretan, Jimmy ^{[1
]}

Georgiopoulos, Michael ^{[1
]}

Koufakou, Anna ^{[1
]}

Cardona, Kel ^{[2
]}

机构：

[1] Univ Cent Florida, Sch Elect Engn & Comp Sci, Orlando, FL 32816 USA

[2] Univ Puerto Rico, Dept Comp Engn, San Juan, PR 00936 USA

来源：

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE | 2010年 / 26卷 / 07期

基金：

美国国家科学基金会;

关键词：

Data mining; Privacy; Distributed architectures; SERVICES;

D O I：

10.1016/j.future.2010.02.017

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

While the emerging field of privacy preserving data mining (PPDM) will enable many new data mining applications, it suffers from several practical difficulties. PPDM algorithms are challenging to develop and computationally intensive to execute. Developers need convenient abstractions to simplify the engineering of PPDM applications. The individual parties involved in the data mining process need a way to bring high-performance, parallel computers to bear on the computationally intensive parts of the PPDM tasks. This paper discusses APHID (Architecture for Private and High-performance Integrated Data mining), a practical architecture and software framework for developing and executing large scale PPDM applications. At one tier, the system supports simplified use of cluster and grid resources, and at another tier, the system abstracts communication for easy PPDM algorithm development. This paper offers a detailed analysis of the challenges in developing PPDM algorithms with existing frameworks, and motivates the design of a new infrastructure based on these challenges. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：891 / 904

页数：14

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