Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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
Selective private function evaluation with applications to private statistics
Proceedings of the twentieth annual ACM symposium on Principles of distributed computing
The Design of Rijndael
Some Privacy Issues in Knowledge Discovery: The OECD Personal Privacy Guidelines
IEEE Expert: Intelligent Systems and Their Applications
Data Swapping: Balancing Privacy against Precision in Mining for Logic Rules
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
Secure Multi-party Computational Geometry
WADS '01 Proceedings of the 7th International Workshop on Algorithms and Data Structures
Collective, Hierarchical Clustering from Distributed, Heterogeneous Data
Revised Papers from Large-Scale Parallel Data Mining, Workshop on Large-Scale Parallel KDD Systems, SIGKDD
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
Collaborative Filtering with Privacy
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
Information sharing across private databases
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
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
Foundations of Cryptography: Volume 2, Basic Applications
Foundations of Cryptography: Volume 2, Basic Applications
When do data mining results violate privacy?
Proceedings of the tenth 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-Sensitive Bayesian Network Parameter Learning
ICDM '04 Proceedings of the Fourth IEEE International Conference on 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
Improved Privacy-Preserving Bayesian Network Parameter Learning on Vertically Partitioned Data
ICDEW '05 Proceedings of the 21st International Conference on Data Engineering Workshops
Fairplay—a secure two-party computation system
SSYM'04 Proceedings of the 13th conference on USENIX Security Symposium - Volume 13
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Public-key cryptosystems based on composite degree residuosity classes
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
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 data mining in the malicious model
International Journal of Information and Computer Security
Impossibility of unconditionally secure scalar products
Data & Knowledge Engineering
Secure two and multi-party association rule mining
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
Communication-Efficient Privacy-Preserving Clustering
Transactions on Data Privacy
Towards privacy-preserving model selection
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
A distributed and privacy-preserving method for network intrusion detection
OTM'10 Proceedings of the 2010 international conference on On the move to meaningful internet systems: Part II
Efficient privacy-preserving data mining in malicious model
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Privacy-preserving data mining in presence of covert adversaries
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Privacy-preserving approach to bayesian network structure learning from distributed data
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Distributed data mining protocols for privacy: a review of some recent results
MADNES'05 Proceedings of the First international conference on Secure Mobile Ad-hoc Networks and Sensors
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Traditionally, many data mining techniques have been designed in the centralized model in which all data is collected and available in one central site. However, as more and more activities are carried out using computers and computer networks, the amount of potentially sensitive data stored by business, governments, and other parties increases. Different parties often wish to benefit from cooperative use of their data, but privacy regulations and other privacy concerns may prevent the parties from sharing their data. Privacy-preserving data mining provides a solution by creating distributed data mining algorithms in which the underlying data need not be revealed. In this paper, we present privacy-preserving protocols for a particular data mining task: learning a Bayesian network from a database vertically partitioned among two parties. In this setting, two parties owning confidential databases wish to learn the Bayesian network on the combination of their databases without revealing anything else about their data to each other. We present an efficient and privacy-preserving protocol to construct a Bayesian network on the parties' joint data.