STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Untraceable electronic mail, return addresses, and digital pseudonyms
Communications of the ACM
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
Mix and Match: Secure Function Evaluation via Ciphertexts
ASIACRYPT '00 Proceedings of the 6th International Conference on the Theory and Application of Cryptology and Information Security: Advances in Cryptology
Revealing information while preserving privacy
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 11th ACM conference on Computer and communications security
Practical privacy: the SuLQ framework
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Privacy-preserving clustering with distributed EM mixture modeling
Knowledge and Information Systems
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Privacy-preserving distributed k-means clustering over arbitrarily partitioned data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Coercion-resistant electronic elections
Proceedings of the 2005 ACM workshop on Privacy in the electronic society
Privacy-preserving distributed association rule mining via semi-trusted mixer
Data & Knowledge Engineering
Secure two-party k-means clustering
Proceedings of the 14th ACM conference on Computer and communications security
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
Civitas: Toward a Secure Voting System
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
Fully homomorphic encryption using ideal lattices
Proceedings of the forty-first annual ACM symposium on Theory of computing
Computing arbitrary functions of encrypted data
Communications of the ACM
A fully homomorphic encryption scheme
A fully homomorphic encryption scheme
Toward basing fully homomorphic encryption on worst-case hardness
CRYPTO'10 Proceedings of the 30th annual conference on Advances in cryptology
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
ESORICS'05 Proceedings of the 10th European conference on Research in Computer Security
Fully homomorphic encryption over the integers
EUROCRYPT'10 Proceedings of the 29th Annual international conference on Theory and Applications of Cryptographic Techniques
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
Least squares quantization in PCM
IEEE Transactions on Information Theory
A public key cryptosystem and a signature scheme based on discrete logarithms
IEEE Transactions on Information Theory
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In recent years, there have been numerous attempts to extend the k-means clustering protocol for single database to a distributed multiple database setting and meanwhile keep privacy of each data site. Current solutions for (whether two or more) multiparty k-means clustering, built on one or more secure two-party computation algorithms, are not equally contributory, in other words, each party does not equally contribute to k-means clustering. This may lead a perfidious attack where a party who learns the outcome prior to other parties tells a lie of the outcome to other parties. In this paper, we present an equally contributory multiparty k-means clustering protocol for vertically partitioned data, in which each party equally contributes to k-means clustering. Our protocol is built on ElGamal's encryption scheme, Jakobsson and Juels's plaintext equivalence test protocol, and mix networks, and protects privacy in terms that each iteration of k-means clustering can be performed without revealing the intermediate values.