Verifiable secret sharing and multiparty protocols with honest majority
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
Computationally private information retrieval (extended abstract)
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Protecting data privacy in private information retrieval schemes
STOC '98 Proceedings of the thirtieth 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
Communications of the ACM
Machine Learning
Executing SQL over encrypted data in the database-service-provider model
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Machine Learning
Cryptographic techniques for privacy-preserving data mining
ACM SIGKDD Explorations Newsletter
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
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
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Building decision tree classifier on private data
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
Information sharing across private databases
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Rights protection for relational data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
A system for watermarking relational databases
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Using randomized response techniques for privacy-preserving data mining
Proceedings of the ninth 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
A Framework for High-Accuracy Privacy-Preserving Mining
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Privacy Preserving Query Processing Using Third Parties
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Privacy-preserving indexing of documents on the network
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Vision paper: enabling privacy for the paranoids
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
A privacy-preserving index for range queries
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Resilient rights protection for sensor streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Computationally private information retrieval with polylogarithmic communication
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
Classification rule discovery for the aviation incidents resulted in fatality
Knowledge-Based Systems
Data mining effect in peer-to-peer queries routing
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
Arbitrarily distributed data-based recommendations with privacy
Data & Knowledge Engineering
Estimating NBC-based recommendations on arbitrarily partitioned data with privacy
Knowledge-Based Systems
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Data mining over multiple data sources has emerged as an important practical problem with applications in different areas such as data streams, data-warehouses, and bioinformatics. Although the data sources are willing to run data mining algorithms in these cases, they do not want to reveal any extra information about their data to other sources due to legal or competition concerns. One possible solution to this problem is to use cryptographic methods. However, the computation and communication complexity of such solutions render them impractical when a large number of data sources are involved. In this paper, we consider a scenario where multiple data sources are willing to run data mining algorithms over the union of their data as long as each data source is guaranteed that its information that does not pertain to another data source will not be revealed. We focus on the classification problem in particular and present an efficient algorithm for building a decision tree over an arbitrary number of distributed sources in a privacy preserving manner using the ID3 algorithm.