Title
Trust-but-Verify: Verifying Result Correctness of Outsourced Frequent Itemset Mining in Data-mining-as-a-service Paradigm
Abstract
Cloud computing is popularizing the computing paradigm in which data is outsourced to a third-party service provider (server) for data mining. 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 specific task of frequent itemset mining. We consider the server that is potentially untrusted and tries to escape from verification by using its prior knowledge of the outsourced data. We propose efficient probabilistic and deterministic verification approaches to check whether the server has returned correct and complete frequent itemsets. Our probabilistic approach can catch incorrect results with high probability, while our deterministic approach measures the result correctness with 100% certainty. We also design efficient verification methods for both cases that the data and the mining setup are updated. We demonstrate the effectiveness and efficiency of our methods using an extensive set of empirical results on real datasets.
Year
DOI
Venue
2016
10.1109/TSC.2015.2436387
IEEE Trans. Services Computing
Keywords
Field
DocType
cloud computing,data mining as a service,result integrity verification,security
Data mining,Certainty,Computer science,Server,Correctness,Outsourcing,Service provider,Probabilistic logic,Deterministic system (philosophy),Database,Cloud computing
Journal
Volume
Issue
ISSN
PP
99
1939-1374
Citations 
PageRank 
References 
4
0.40
16
Authors
3
Name
Order
Citations
PageRank
Dong, B.140.40
Liu, R.240.40
Wendy Hui Wang313313.82