A public key cryptosystem and a signature scheme based on discrete logarithms
Proceedings of CRYPTO 84 on Advances in cryptology
The knowledge complexity of interactive proof systems
SIAM Journal on Computing
Neural network learning and expert systems
Neural network learning and expert systems
The cascade-correlation learning architecture
Advances in neural information processing systems 2
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
Privacy Preserving Data Mining
CRYPTO '00 Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
Foundations of Cryptography: Volume 2, Basic Applications
Foundations of Cryptography: Volume 2, Basic Applications
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
The Applicability of Recurrent Neural Networks for Biological Sequence Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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 secure distributed framework for achieving k-anonymity
The VLDB Journal — The International Journal on Very Large Data Bases
A privacy-preserving protocol for neural-network-based computation
MM&Sec '06 Proceedings of the 8th workshop on Multimedia and security
Oblivious polynomial evaluation and oblivious neural learning
Theoretical Computer Science
Privacy-preservation for gradient descent methods
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy preserving clustering on horizontally partitioned data
Data & Knowledge Engineering
On static and dynamic methods for condensation-based privacy-preserving data mining
ACM Transactions on Database Systems (TODS)
Oblivious neural network computing via homomorphic encryption
EURASIP Journal on Information Security
Guest editorial: Recent advances in preserving privacy when mining data
Data & Knowledge Engineering
The applicability of the perturbation based privacy preserving data mining for real-world data
Data & Knowledge Engineering
Distributed privacy preserving k-means clustering with additive secret sharing
PAIS '08 Proceedings of the 2008 international workshop on Privacy and anonymity in information society
Protocols for secure computations
SFCS '82 Proceedings of the 23rd Annual Symposium on Foundations of Computer Science
Data privacy protection in multi-party clustering
Data & Knowledge Engineering
Privacy-preserving data mining in the malicious model
International Journal of Information and Computer Security
Privacy-preserving data publishing for cluster analysis
Data & Knowledge Engineering
Privacy preserving ID3 using Gini Index over horizontally partitioned data
AICCSA '08 Proceedings of the 2008 IEEE/ACS International Conference on Computer Systems and Applications
Privacy-Preserving Singular Value Decomposition
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Accurate and large-scale privacy-preserving data mining using the election paradigm
Data & Knowledge Engineering
Formal anonymity models for efficient privacy-preserving joins
Data & Knowledge Engineering
Privacy-preserving backpropagation neural network learning
IEEE Transactions on Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Public-key cryptosystems based on composite degree residuosity classes
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
Large-scale k-means clustering with user-centric privacy preservation
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Suitable MLP Network Activation Functions for Breast Cancer and Thyroid Disease Detection
CIMSIM '10 Proceedings of the 2010 Second International Conference on Computational Intelligence, Modelling and Simulation
Privacy preserving Back-propagation neural network learning over arbitrarily partitioned data
Neural Computing and Applications
Privacy-Preserving decision trees over vertically partitioned data
DBSec'05 Proceedings of the 19th annual IFIP WG 11.3 working conference on Data and Applications Security
Privacy-Preserving collaborative association rule mining
DBSec'05 Proceedings of the 19th annual IFIP WG 11.3 working conference on Data and Applications Security
On private scalar product computation for privacy-preserving data mining
ICISC'04 Proceedings of the 7th international conference on Information Security and Cryptology
Cancelable Biometrics Realization With Multispace Random Projections
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Perceptron-based learning algorithms
IEEE Transactions on Neural Networks
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Neural network systems are highly capable of deriving knowledge from complex data, and they are used to extract patterns and trends which are otherwise hidden in many applications. Preserving the privacy of sensitive data and individuals' information is a major challenge in many of these applications. One of the most popular algorithms in neural network learning systems is the back-propagation (BP) algorithm, which is designed for single-layer and multi-layer models and can be applied to continuous data and differentiable activation functions. Another recently introduced learning technique is the extreme learning machine (ELM) algorithm. Although it works only on single-layer models, ELM can out-perform the BP algorithm by reducing the communication required between parties in the learning phase. In this paper, we present new privacy-preserving protocols for both the BP and ELM algorithms when data is horizontally and vertically partitioned among several parties. These new protocols, which preserve the privacy of both the input data and the constructed learning model, can be applied to online incoming records and/or batch learning. Furthermore, the final model is securely shared among all parties, who can use it jointly to predict the corresponding output for their target data.