Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Foundations of Cryptography: Basic Tools
Foundations of Cryptography: Basic Tools
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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
Using randomized response techniques for privacy-preserving data mining
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
Order preserving encryption for numeric data
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
GeneScout: a data mining system for predicting vertebrate genes in genomic DNA sequences
Information Sciences: an International Journal - Special issue: Soft computing data mining
Privacy-preserving Bayesian network structure computation on distributed heterogeneous data
Proceedings of the tenth 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
Privately computing a distributed k-nn classifier
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Deriving private information from randomized data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Anonymity-preserving data collection
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Privacy-preserving distributed k-means clustering over arbitrarily partitioned data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Fuzzy reasoning model under quotient space structure
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Dealing with uncertainty in data mining and information extraction
Efficient anonymity-preserving data collection
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Fairplay—a secure two-party computation system
SSYM'04 Proceedings of the 13th conference on USENIX Security Symposium - Volume 13
Transforming Semi-Honest Protocols to Ensure Accountability
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
On private scalar product computation for privacy-preserving data mining
ICISC'04 Proceedings of the 7th international conference on Information Security and Cryptology
A false negative approach to mining frequent itemsets from high speed transactional data streams
Information Sciences: an International Journal
Towards the evaluation of time series protection methods
Information Sciences: an International Journal
Information Sciences: an International Journal
Hiding collaborative recommendation association rules on horizontally partitioned data
Intelligent Data Analysis
Privacy-preserving data mining: A feature set partitioning approach
Information Sciences: an International Journal
Learning latent variable models from distributed and abstracted data
Information Sciences: an International Journal
Privacy-preserving disjunctive normal form operations on distributed sets
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
Bands of privacy preserving objectives: classification of PPDM strategies
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
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Privacy preserving data mining addresses the need of multiple parties with private inputs to run a data mining algorithm and learn the results over the combined data without revealing any unnecessary information. Most of the existing cryptographic solutions to privacy-preserving data mining assume semi-honest participants. In theory, these solutions can be extended to the malicious model using standard techniques like commitment schemes and zero-knowledge proofs. However, these techniques are often expensive, especially when the data sizes are large. In this paper, we investigate alternative ways to convert solutions in the semi-honest model to the malicious model. We take two classical solutions as examples, one of which can be extended to the malicious model with only slight modifications while another requires a careful redesign of the protocol. In both cases, our solutions for the malicious model are much more efficient than the zero-knowledge proofs based solutions.