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
Copyright Status
Unknown
Socpus ID
77955717757 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/77955717757
STARS Citation
Secretan, Jimmy; Georgiopoulos, Michael; Koufakou, Anna; and Cardona, Kel, "Aphid: An Architecture For Private, High-Performance Integrated Data Mining" (2010). Scopus Export 2010-2014. 1148.
https://stars.library.ucf.edu/scopus2010/1148