k-anonymity: a model for protecting privacy
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
Gossip-Based Computation of Aggregate Information
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
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
Privacy-preserving distributed k-means clustering over arbitrarily partitioned data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
Public-key cryptosystems based on composite degree residuosity classes
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
ESORICS'05 Proceedings of the 10th European conference on Research in Computer Security
Privacy-preserving reinforcement learning
Proceedings of the 25th international conference on Machine learning
Privacy-preserving back-propagation and extreme learning machine algorithms
Data & Knowledge Engineering
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A k-means clustering with new privacy-preserving concept, user-centric privacy preservation, is presented. In this framework, users can conduct data mining using their private information with storing them in their local storages. After the computation, they obtain only mining result without disclosing private information to others. The number of parties that join conventional privacy-preserving data mining has been assumed to be two. In our framework, we assume large numbers of parties join the protocol, therefore, not only scalability but also asynchronism and fault-tolerance is important. Considering this, we propose a k-mean algorithm combined with a decentralized cryptographic protocol and a gossip-based protocol. The computational complexity is O(log n) with respect to the number of parties n and experimental results show that our protocol is scalable even with one million parties.