Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Building decision tree classifier on private data
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
Privacy-Preserving Data Mining on Data Grids in the Presence of Malicious Participants
HPDC '04 Proceedings of the 13th IEEE International Symposium on High Performance Distributed Computing
Privately computing a distributed k-nn classifier
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Privacy-preserving clustering with distributed EM mixture modeling
Knowledge and Information Systems
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
Foundations of Cryptography: Volume 1
Foundations of Cryptography: Volume 1
Fairplay—a secure two-party computation system
SSYM'04 Proceedings of the 13th conference on USENIX Security Symposium - Volume 13
Public-key cryptosystems based on composite degree residuosity classes
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
Privacy-preserving set operations
CRYPTO'05 Proceedings of the 25th annual international conference on Advances in Cryptology
TCC'06 Proceedings of the Third conference on Theory of Cryptography
Efficient privacy-preserving data mining in malicious model
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Privacy-preserving data mining in presence of covert adversaries
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Privacy-preserving data mining: a game-theoretic approach
DBSec'11 Proceedings of the 25th annual IFIP WG 11.3 conference on Data and applications security and privacy
Anonymity meets game theory: secure data integration with malicious participants
The VLDB Journal — The International Journal on Very Large Data Bases
Privacy-preserving statistical analysis on ubiquitous health data
TrustBus'11 Proceedings of the 8th international conference on Trust, privacy and security in digital business
Efficient Protocols for Principal Eigenvector Computation over Private Data
Transactions on Data Privacy
Privacy-preserving back-propagation and extreme learning machine algorithms
Data & Knowledge Engineering
Privacy preserving neural networks in iris signature feature extraction
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
Proceedings of the 7th ACM Symposium on Information, Computer and Communications Security
Low Dimensional Data Privacy Preservation Using Multi Layer Artificial Neural Network
International Journal of Intelligent Information Technologies
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Most of the cryptographic work in privacy-preserving distributed data mining deals with semi-honest adversaries, which are assumed to follow the prescribed protocol but try to infer private information using the messages they receive during the protocol. Although the semi-honest model is reasonable in some cases, it is unrealistic to assume that adversaries will always follow the protocols exactly. In particular, malicious adversaries could deviate arbitrarily from their prescribed protocols. Secure protocols that are developed against malicious adversaries require utilisation of complex techniques. Clearly, protocols that can withstand malicious adversaries provide more security. However, there is an obvious trade-off: protocols that are secure against malicious adversaries are generally more expensive than those secure against semi-honest adversaries only. In this paper, our goal is to make an analysis of trade-offs between performance and security in privacy-preserving distributed data mining algorithms in the two models. In order to make a realistic comparison, we enhance commonly used subprotocols that are secure in the semi-honest model with zero knowledge proofs to be secure in the malicious model. We compare the performance of these protocols in both models.