Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A method for obtaining digital signatures and public-key cryptosystems
Communications of the ACM
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Cryptographic techniques for privacy-preserving data mining
ACM SIGKDD Explorations Newsletter
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
Secure kNN computation on encrypted databases
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Taking account of privacy when designing cloud computing services
CLOUD '09 Proceedings of the 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing
Using sub-sequence information with kNN for classification of sequential data
ICDCIT'05 Proceedings of the Second international conference on Distributed Computing and Internet Technology
An efficient and secure data sharing framework using homomorphic encryption in the cloud
Proceedings of the 1st International Workshop on Cloud Intelligence
Secure k-NN computation on encrypted cloud data without sharing key with query users
Proceedings of the 2013 international workshop on Security in cloud computing
Secure k-NN query on encrypted cloud database without key-sharing
International Journal of Electronic Security and Digital Forensics
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Due to increased adoption of cloud computing, there is a growing need of addressing the data privacy during mining. On the other hand, knowledge sharing is a key to survive many business organizations. Several attempts have been made to mine the data in distributed environment however, maintaining the privacy while mining the data over cloud is a challenging task. In this paper, we present an efficient and practical cryptographic based scheme that preserves privacy and mine the cloud data which is distributed in nature. In order to address the classification task, our approach uses k-NN classifier. We extend the Jaccard measure to find the similarity between two encrypted and distributed records by conducting an equality test. In addition, our approach accelerates mining by finding nearest neighbours at local and then at global level. The proposed approach avoids transmitting the original data and sharing of the key that is required in traditional crypto based privacy preserving data mining solutions.