Title

Aphid: An Architecture For Private, High-Performance Integrated Data Mining

Keywords

Data mining; Distributed architectures; Privacy

Abstract

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. © 2010 Elsevier B.V. All rights reserved.

Publication Date

7-1-2010

Publication Title

Future Generation Computer Systems

Volume

26

Issue

7

Number of Pages

891-904

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.future.2010.02.017

Socpus ID

77955717757 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/77955717757

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