A randomized protocol for signing contracts
Communications of the ACM
An efficient probabilistic public key encryption scheme which hides all partial information
Proceedings of CRYPTO 84 on Advances in cryptology
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Secret sharing homomorphisms: keeping shares of a secret secret
Proceedings on Advances in cryptology---CRYPTO '86
A communication-privacy tradeoff for modular addition
Information Processing Letters
A new public key cryptosystem based on higher residues
CCS '98 Proceedings of the 5th ACM conference on Computer and communications security
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
Efficient oblivious transfer protocols
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Secure multi-party computation problems and their applications: a review and open problems
Proceedings of the 2001 workshop on New security paradigms
Machine Learning
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
Building decision tree classifier on private data
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
An architecture for privacy-preserving mining of client information
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
A Secure Protocol for Computing Dot-Products in Clustered and Distributed Environments
ICPP '02 Proceedings of the 2002 International Conference on Parallel Processing
Privacy-Preserving Cooperative Statistical Analysis
ACSAC '01 Proceedings of the 17th Annual Computer Security Applications Conference
On the Privacy Preserving Properties of Random Data Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on 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
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 clustering with distributed EM mixture modeling
Knowledge and Information Systems
Deriving private information from randomized data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Privacy-preserving distributed k-means clustering over arbitrarily partitioned data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Secure set intersection cardinality with application to association rule mining
Journal of Computer Security
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
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 decision trees over vertically partitioned data
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
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
An approach of private classification on vertically partitioned data
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
Collusion-resistant privacy-preserving data mining
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Non-black-box computation of linear regression protocols with malicious adversaries
ISPEC'11 Proceedings of the 7th international conference on Information security practice and experience
TrustBus'11 Proceedings of the 8th international conference on Trust, privacy and security in digital business
Privacy preserving feature selection for distributed data using virtual dimension
Proceedings of the 20th ACM international conference on Information and knowledge management
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part I
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Privacy-preserving genetic algorithms for rule discovery
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Estimating NBC-based recommendations on arbitrarily partitioned data with privacy
Knowledge-Based Systems
Distributed and Parallel Databases
Secure outsourced computation of iris matching
Journal of Computer Security
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Privacy-preserving data mining--developing models without seeing the data --- is receiving growing attention. This paper assumes a privacy-preserving distributed data mining scenario: data sources collaborate to develop a global model, but must not disclose their data to others. The problem of secure distributed classification is an important one. In many situations, data is split between multiple organizations. These organizations may want to utilize all of the data to create more accurate predictive models while revealing neither their training data/databases nor the instances to be classified. Naïve Bayes is often used as a baseline classifier, consistently providing reasonable classification performance. This paper brings privacy-preservation to that baseline, presenting protocols to develop a Naïve Bayes classifier on both vertically as well as horizontally partitioned data.