STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
The basic ideas in neural networks
Communications of the ACM
Oblivious transfer and polynomial evaluation
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Foundations of Cryptography: Basic Tools
Foundations of Cryptography: Basic Tools
CRYPTO '89 Proceedings of the 9th Annual International Cryptology Conference on Advances in Cryptology
The Decision Diffie-Hellman Problem
ANTS-III Proceedings of the Third International Symposium on Algorithmic Number Theory
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
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Using randomized response techniques for privacy-preserving data mining
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
A new scheme on privacy-preserving data classification
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
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 Data Classification with Rotation Perturbation
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data
Proceedings of the 2006 ACM symposium on Applied computing
Cryptographically private support vector machines
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A privacy-preserving protocol for neural-network-based computation
MM&Sec '06 Proceedings of the 8th workshop on Multimedia and security
Foundations of Cryptography: Volume 1
Foundations of Cryptography: Volume 1
Fairplay—a secure two-party computation system
SSYM'04 Proceedings of the 13th conference on USENIX Security Symposium - Volume 13
Privacy-preservation for gradient descent methods
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Protocols for secure computations
SFCS '82 Proceedings of the 23rd Annual Symposium on Foundations of Computer Science
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
On private scalar product computation for privacy-preserving data mining
ICISC'04 Proceedings of the 7th international conference on Information Security and Cryptology
Quadratic error minimization in a distributed environment with privacy preserving
PSDML'10 Proceedings of the international ECML/PKDD conference on Privacy and security issues in data mining and machine learning
Privacy Preserving Aggregation of Secret Classifiers
Transactions on Data Privacy
Privacy-preserving back-propagation and extreme learning machine algorithms
Data & Knowledge Engineering
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With the development of distributed computing environment, many learning problems now have to deal with distributed input data. To enhance cooperations in learning, it is important to address the privacy concern of each data holder by extending the privacy preservation notion to original learning algorithms. In this paper, we focus on preserving the privacy in an important learning model, multilayer neural networks. We present a privacy-preserving two-party distributed algorithm of backpropagation which allows a neural network to be trained without requiring either party to reveal her data to the other. We provide complete correctness and security analysis of our algorithms. The effectiveness of our algorithms is verified by experiments on various real world data sets.