STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Data mining and KDD: promise and challenges
Future Generation Computer Systems - Special double issue on data mining
Oblivious transfer and polynomial evaluation
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Papyrus: a system for data mining over local and wide area clusters and super-clusters
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Machine Learning
A Data Mining Architecture for Distributed Environments
IICS '02 Proceedings of the Second International Workshop on Innovative Internet Computing Systems
Distributed data mining on the grid
Future Generation Computer Systems - Grid computing: Towards a new computing infrastructure
Cryptographic techniques for privacy-preserving data mining
ACM SIGKDD Explorations Newsletter
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Investigating the Limits of SOAP Performance for Scientific Computing
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
Building decision tree classifier on private data
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
Issues of agent-based distributed data mining
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Privacy-Preserving Cooperative Statistical Analysis
ACSAC '01 Proceedings of the 17th Annual Computer Security Applications Conference
Developing Distributed Data Mining Implementations for a Grid Environment
CCGRID '02 Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid
An Architecture to Support Distributed Data Mining Services in E-Commerce Environments
WECWIS '00 Proceedings of the Second International Workshop on Advance Issues of E-Commerce and Web-Based Information Systems (WECWIS 2000)
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-preserving Bayesian network structure computation on distributed heterogeneous data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
Privacy-Preserving Data Mining: Why, How, and When
IEEE Security and Privacy
Privately computing a distributed k-nn classifier
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
A Framework for High-Accuracy Privacy-Preserving Mining
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Privacy-preserving distributed k-means clustering over arbitrarily partitioned data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Privacy Preserving Clustering on Horizontally Partitioned Data
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data
Proceedings of the 2006 ACM symposium on Applied computing
Improved Privacy-Preserving Bayesian Network Parameter Learning on Vertically Partitioned Data
ICDEW '05 Proceedings of the 21st International Conference on Data Engineering Workshops
Distributed Data Mining in Peer-to-Peer Networks
IEEE Internet Computing
Optimal Grid Exploitation Algorithms for Data Mining
ISPDC '06 Proceedings of the Proceedings of The Fifth International Symposium on Parallel and Distributed Computing
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
From access and integration to mining of secure genomic data sets across the Grid
Future Generation Computer Systems
Interpreting the data: Parallel analysis with Sawzall
Scientific Programming - Dynamic Grids and Worldwide Computing
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Fairplay—a secure two-party computation system
SSYM'04 Proceedings of the 13th conference on USENIX Security Symposium - Volume 13
A domain-specific programming language for secure multiparty computation
Proceedings of the 2007 workshop on Programming languages and analysis for security
Dryad: distributed data-parallel programs from sequential building blocks
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
A grid-based approach for enterprise-scale data mining
Future Generation Computer Systems - Special section: Data mining in grid computing environments
Distributed data mining services leveraging WSRF
Future Generation Computer Systems - Special section: Data mining in grid computing environments
Design and implementation of a data mining grid-aware architecture
Future Generation Computer Systems - Special section: Data mining in grid computing environments
A grid-based approach for enterprise-scale data mining
Future Generation Computer Systems - Special section: Data mining in grid computing environments
Distributed data mining on agent grid: issues, platform and development toolkit
Future Generation Computer Systems - Special section: Data mining in grid computing environments
Distributed data mining in grid computing environments
Future Generation Computer Systems - Special section: Data mining in grid computing environments
SPC: a distributed, scalable platform for data mining
Proceedings of the 4th international workshop on Data mining standards, services and platforms
Grid-enabling data mining applications with DataMiningGrid: An architectural perspective
Future Generation Computer Systems
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
Webservices oriented data mining in knowledge architecture
Future Generation Computer Systems
Public-key cryptosystems based on composite degree residuosity classes
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
Privacy-Preserving SVM classification on vertically partitioned data
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Hierarchical infrastructure for large-scale distributed privacy-preserving data mining
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III
On private scalar product computation for privacy-preserving data mining
ICISC'04 Proceedings of the 7th international conference on Information Security and Cryptology
Distributed data mining on grids: services, tools, and applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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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 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.