The nature of statistical learning theory
The nature of statistical learning theory
Privacy preserving auctions and mechanism design
Proceedings of the 1st ACM conference on Electronic commerce
Foundations of Cryptography: Volume 2, Basic Applications
Foundations of Cryptography: Volume 2, Basic Applications
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Privacy-preserving Bayesian network structure computation on distributed heterogeneous data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Privately computing a distributed k-nn classifier
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data
Proceedings of the 2006 ACM symposium on Applied computing
Fairplay—a secure two-party computation system
SSYM'04 Proceedings of the 13th conference on USENIX Security Symposium - Volume 13
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
An oblivious transfer protocol with log-squared communication
ISC'05 Proceedings of the 8th international conference on Information Security
On private scalar product computation for privacy-preserving data mining
ICISC'04 Proceedings of the 7th international conference on Information Security and Cryptology
Oblivious neural network computing via homomorphic encryption
EURASIP Journal on Information Security
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
Privacy-preserving cox regression for survival analysis
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-preserving classification of vertically partitioned data via random kernels
ACM Transactions on Knowledge Discovery from Data (TKDD)
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Privacy-preserving backpropagation neural network learning
IEEE Transactions on Neural Networks
Towards privacy-preserving model selection
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
ICNC'09 Proceedings of the 5th international conference on Natural computation
Privacy-preserving outsourcing support vector machines with random transformation
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Differentially Private Empirical Risk Minimization
The Journal of Machine Learning Research
Preserving privacy of moving objects via temporal clustering of spatio-temporal data streams
Proceedings of the 4th ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS
Privacy-preserving back-propagation and extreme learning machine algorithms
Data & Knowledge Engineering
Privacy-preserving genetic algorithms for rule discovery
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Cloud-enabled privacy-preserving collaborative learning for mobile sensing
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
International Journal of Data Warehousing and Mining
| Hi-index | 0.00 |
We propose private protocols implementing the Kernel Adatron and Kernel Perceptron learning algorithms, give private classification protocols and private polynomial kernel computation protocols. The new protocols return their outputs - either the kernel value, the classifier or the classifications - in encrypted form so that they can be decrypted only by a common agreement by the protocol participants. We show how to use the encrypted classifications to privately estimate many properties of the data and the classifier. The new SVM classifiers are the first to be proven private according to the standard cryptographic definitions.