Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
A Genetic Algorithm With Sequential Niching For Discovering Small-disjunct Rules
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Privacy Preserving Data Mining
CRYPTO '00 Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology
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 Cooperative Statistical Analysis
ACSAC '01 Proceedings of the 17th Annual Computer Security Applications Conference
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 distributed k-means clustering over arbitrarily partitioned data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in 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
Secure set intersection cardinality with application to association rule mining
Journal of Computer Security
Privacy-preserving Naïve Bayes classification
The VLDB Journal — The International Journal on Very Large Data Bases
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
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
On private scalar product computation for privacy-preserving data mining
ICISC'04 Proceedings of the 7th international conference on Information Security and Cryptology
Privacy-preserving linear fisher discriminant analysis
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Privacy-preserving multi-objective evolutionary algorithms
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Arbitrarily distributed data-based recommendations with privacy
Data & Knowledge Engineering
Estimating NBC-based recommendations on arbitrarily partitioned data with privacy
Knowledge-Based Systems
| Hi-index | 0.00 |
Decision tree induction algorithms generally adopt a greedy approach to select attributes in order to optimize some criteria at each iteration of the tree induction process. When a decision tree has been constructed, a set of decision rules may be correspondingly derived. Univariate decision tree induction algorithms generally yield the same tree regardless of how many times it is induced from the same training data set. Genetic algorithms have been shown to discover a better set of rules, albeit at the expense of efficiency. In this paper, we propose a protocol for secure genetic algorithms for the following scenario: Two parties, each holding an arbitrarily partitioned data set, seek to perform genetic algorithms to discover a better set of rules without disclosing their own private data. The challenge for privacy-preserving genetic algorithms is to allow the two parties to securely and jointly evaluate the fitness value of each chromosome using each party's private data but without compromising their data privacy. We propose a new protocol to address this challenge that is correct and secure. The proposed protocol is not only privacy-preserving at each iteration of the genetic algorithm, the intermediate results generated at each iteration do not compromise the data privacy of the participating parties.