STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Secure databases: protection against user influence
ACM Transactions on Database Systems (TODS)
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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
Building decision tree classifier on private data
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
Computational disclosure control: a primer on data privacy protection
Computational disclosure control: a primer on data privacy protection
When do data mining results violate privacy?
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Privately computing a distributed k-nn classifier
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Protocols for secure computations
SFCS '82 Proceedings of the 23rd Annual Symposium on Foundations of Computer Science
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
A secure distributed framework for achieving k-anonymity
The VLDB Journal — The International Journal on Very Large Data Bases
Data & Knowledge Engineering
Privacy-preserving data mashup
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
Allowing privacy protection algorithms to jump out of local optimums: an ordered greed framework
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
Centralized and Distributed Anonymization for High-Dimensional Healthcare Data
ACM Transactions on Knowledge Discovery from Data (TKDD)
Anonymity meets game theory: secure data integration with malicious participants
The VLDB Journal — The International Journal on Very Large Data Bases
Cloning for privacy protection in multiple independent data publications
Proceedings of the 20th ACM international conference on Information and knowledge management
A graph enrichment based clustering over vertically partitioned data
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
Distributed data federation without disclosure of user existence
DBSec'12 Proceedings of the 26th Annual IFIP WG 11.3 conference on Data and Applications Security and Privacy
t-Plausibility: Generalizing Words to Desensitize Text
Transactions on Data Privacy
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k-anonymity provides a measure of privacy protection by preventing re-identification of data to fewer than a group of k data items. While algorithms exist for producing k-anonymous data, the model has been that of a single source wanting to publish data. This paper presents a k-anonymity protocol when the data is vertically partitioned between sites. A key contribution is a proof that the protocol preserves k-anonymity between the sites: While one site may have individually identifiable data, it learns nothing that violates k-anonymity with respect to the data at the other site. This is a fundamentally different distributed privacy definition than that of Secure Multiparty Computation, and it provides a better match with both ethical and legal views of privacy.