TY - JOUR
T1 - Trust-but-Verify
T2 - Verifying Result Correctness of Outsourced Frequent Itemset Mining in Data-Mining-As-a-Service Paradigm
AU - Dong, Boxiang
AU - Liu, Ruilin
AU - Wang, Hui
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/1/1
Y1 - 2016/1/1
N2 - 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 percent 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.
AB - 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 percent 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.
KW - Cloud computing
KW - data mining as a service
KW - result integrity verification
KW - security
UR - http://www.scopus.com/inward/record.url?scp=84961990551&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84961990551&partnerID=8YFLogxK
U2 - 10.1109/TSC.2015.2436387
DO - 10.1109/TSC.2015.2436387
M3 - Article
AN - SCOPUS:84961990551
SN - 1939-1374
VL - 9
SP - 18
EP - 32
JO - IEEE Transactions on Services Computing
JF - IEEE Transactions on Services Computing
IS - 1
M1 - 7122916
ER -