The knowledge complexity of interactive proof systems
SIAM Journal on Computing
Proof verification and the hardness of approximation problems
Journal of the ACM (JACM)
Executing SQL over encrypted data in the database-service-provider model
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Verifying completeness of relational query results in data publishing
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Journal of Cognitive Neuroscience
Security in outsourcing of association rule mining
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
An audit environment for outsourcing of frequent itemset mining
Proceedings of the VLDB Endowment
k-Support anonymity based on pseudo taxonomy for outsourcing of frequent itemset mining
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Non-interactive verifiable computing: outsourcing computation to untrusted workers
CRYPTO'10 Proceedings of the 30th annual conference on Advances in cryptology
Efficient verification of web-content searching through authenticated web crawlers
Proceedings of the VLDB Endowment
AUDIO: an integrity auditing framework of outlier-mining-as-a-service systems
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
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The data-mining-as-a-service (DMaS) paradigm enables the data owner (client) that lacks expertise or computational resources to outsource its mining tasks to a third-party service provider (server). Outsourcing, however, raises a serious security issue: how can the client of weak computational power verify that the server returned correct mining result? In this paper, we focus on the problem of frequent itemset mining, and propose efficient and practical probabilistic verification approaches to check whether the server has returned correct and complete frequent itemsets.