STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Generalizing data to provide anonymity when disclosing information (abstract)
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Communications of the ACM
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Selective private function evaluation with applications to private statistics
Proceedings of the twentieth annual ACM symposium on Principles of distributed computing
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Secure Multi-party Computational Geometry
WADS '01 Proceedings of the 7th International Workshop on Algorithms and Data Structures
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
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
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
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Privacy-enhancing k-anonymization of customer data
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Anonymity-preserving data collection
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in 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 Computation of Bayesian Networks on Vertically Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
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
Efficient receipt-free voting based on homomorphic encryption
EUROCRYPT'00 Proceedings of the 19th 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 publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
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With the rapid advance of the Internet, a large amount of sensitive data is collected, stored, and processed by different parties. Data mining is a powerful tool that can extract knowledge from large amounts of data. Generally, data mining requires that data be collected into a central site. However, privacy concerns may prevent different parties from sharing their data with others. Cryptography provides extremely powerful tools which enable data sharing while protecting data privacy. In this paper, we briefly survey four recently proposed cryptographic techniques for protecting data privacy in distributed settings. First, we describe a privacy-preserving technique for learning Bayesian networks from a dataset vertically partitioned between two parties. Then, we describe three privacy-preserving data mining techniques in a fully distributed setting where each customer holds a single data record of the database.