Practical data-swapping: the first steps
ACM Transactions on Database Systems (TODS)
Identity-based cryptosystems and signature schemes
Proceedings of CRYPTO 84 on Advances in cryptology
Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
A zero-one law for Boolean privacy
SIAM Journal on Discrete Mathematics
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
A security machanism for statistical database
ACM Transactions on Database Systems (TODS)
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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
The statistical security of a statistical database
ACM Transactions on Database Systems (TODS)
Statistical Databases: Characteristics, Problems, and some Solutions
VLDB '82 Proceedings of the 8th International Conference on Very Large Data Bases
Identity-Based Encryption from the Weil Pairing
CRYPTO '01 Proceedings of the 21st Annual International Cryptology Conference on Advances in Cryptology
Revealing information while preserving privacy
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Limiting privacy breaches in privacy preserving data mining
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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
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
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
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Auditing and Inference Control in Statistical Databases
IEEE Transactions on Software Engineering
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
Privacy and communication complexity
SFCS '89 Proceedings of the 30th Annual Symposium on Foundations of Computer Science
Efficient identity-based encryption without random oracles
EUROCRYPT'05 Proceedings of the 24th annual international conference on Theory and Applications of Cryptographic Techniques
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When a database owner needs to disclose her data, she can k-anonymize her data to protect the involved individuals' privacy. However, if the data is distributed between two owners, then it is an open question whether the two owners can jointly k-anonymize the union of their data, such that the information suppressed in one owner's data is not revealed to the other owner. In this paper, we study this problemof distributed k-anonymization. We have two major results: First, it is impossible to design an unconditionally private protocol that implements any normal k-anonymization function, where normal k-anonymization functions are a very broad class of k-anonymization functions. Second, we give an efficent protocol that implements a normal k-anonymization function and show that it is private against polynomial-time adversaries. Our results have many potential applications and can be extended to three or more parties.