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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 difficult to develop and computationally intensive to execute. Developers need convenient abstractions to reduce the costs of engineering 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 software architecture for developing and executing large-scale PPDM applications. At one level, the system supports simplified use of cluster and grid resources, and at another level, the system abstracts communication for easy PPDM algorithm development.
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