The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Uncheatable Distributed Computations
CT-RSA 2001 Proceedings of the 2001 Conference on Topics in Cryptology: The Cryptographer's Track at RSA
Secure Distributed Computing in a Commercial Environment
FC '01 Proceedings of the 5th International Conference on Financial Cryptography
Hardening Functions for Large Scale Distributed Computations
SP '03 Proceedings of the 2003 IEEE Symposium on Security and Privacy
Result Verification and Trust-Based Scheduling in Peer-to-Peer Grids
P2P '05 Proceedings of the Fifth IEEE International Conference on Peer-to-Peer Computing
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
SecureMR: A Service Integrity Assurance Framework for MapReduce
ACSAC '09 Proceedings of the 2009 Annual Computer Security Applications Conference
Airavat: security and privacy for MapReduce
NSDI'10 Proceedings of the 7th USENIX conference on Networked systems design and implementation
A model of computation for MapReduce
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
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Recent development in Internet-scale data applications and services, combined with the proliferation of cloud computing, has created a new computing model for data intensive computing best characterized by the MapReduce paradigm. The MapReduce computing paradigm, pioneered by Google in its Internet search application, is an architectural and programming model for efficiently processing massive amount of raw unstructured data. With the availability of the open source Hadoop tools, applications built based on the MapReduce computing model are rapidly growing. In this work, we focus on a unique security concern on the MapReduce architecture. Given the potential security risks from lazy or malicious servers involved in a MapReduce task, we design efficient and innovative mechanisms for detecting cheating services under the MapReduce environment based on watermark injection and random sampling methods. The new detection schemes are expected to significantly reduce the cost of verification overhead. Finally, extensive analytical and experimental evaluation confirms the effectiveness of our schemes in MapReduce result verification.