VAWS: Constructing Trusted Open Computing System of MapReduce with Verified Participants

Yan DING; Huaimin WANG; Lifeng WEI; Songzheng CHEN; Hongyi FU; Xinhai XU

doi:10.1587/transinf.E97.D.721

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VAWS: Constructing Trusted Open Computing System of MapReduce with Verified Participants

Yan DING, Huaimin WANG, Lifeng WEI, Songzheng CHEN, Hongyi FU, Xinhai XU

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MapReduce is commonly used as a parallel massive data processing model. When deploying it as a service over the open systems, the computational integrity of the participants is becoming an important issue due to the untrustworthy workers. Current duplication-based solutions can effectively solve non-collusive attacks, yet most of them require a centralized worker to re-compute additional sampled tasks to defend collusive attacks, which makes the worker a bottleneck. In this paper, we try to explore a trusted worker scheduling framework, named VAWS, to detect collusive attackers and assure the integrity of data processing without extra re-computation. Based on the historical results of verification, we construct an Integrity Attestation Graph (IAG) in VAWS to identify malicious mappers and remove them from the framework. To further improve the efficiency of identification, a verification-couple selection method with the IAG guidance is introduced to detect the potential accomplices of the confirmed malicious worker. We have proven the effectiveness of our proposed method on the improvement of system performance in theoretical analysis. Intensive experiments show the accuracy of VAWS is over 97% and the overhead of computation is closed to the ideal value of 2 with the increasing of the number of map tasks in our scheme.

Publication: IEICE TRANSACTIONS on Information Vol.E97-D No.4 pp.721-732

Publication Date: 2014/04/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1587/transinf.E97.D.721

Type of Manuscript: Special Section PAPER (Special Section on Data Engineering and Information Management)

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Authors

Yan DING
  National University of Defense Technology
Huaimin WANG
  National Univ. of Defense Technology
Lifeng WEI
  National University of Defense Technology
Songzheng CHEN
  National University of Defense Technology
Hongyi FU
  National University of Defense Technology
Xinhai XU
  National University of Defense Technology

Keyword

result verification, computational integrity, MapReduce, open system, integrity attestation graph

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Yan DING, Huaimin WANG, Lifeng WEI, Songzheng CHEN, Hongyi FU, Xinhai XU, "VAWS: Constructing Trusted Open Computing System of MapReduce with Verified Participants" in IEICE TRANSACTIONS on Information, vol. E97-D, no. 4, pp. 721-732, April 2014, doi: 10.1587/transinf.E97.D.721.
Abstract: MapReduce is commonly used as a parallel massive data processing model. When deploying it as a service over the open systems, the computational integrity of the participants is becoming an important issue due to the untrustworthy workers. Current duplication-based solutions can effectively solve non-collusive attacks, yet most of them require a centralized worker to re-compute additional sampled tasks to defend collusive attacks, which makes the worker a bottleneck. In this paper, we try to explore a trusted worker scheduling framework, named VAWS, to detect collusive attackers and assure the integrity of data processing without extra re-computation. Based on the historical results of verification, we construct an Integrity Attestation Graph (IAG) in VAWS to identify malicious mappers and remove them from the framework. To further improve the efficiency of identification, a verification-couple selection method with the IAG guidance is introduced to detect the potential accomplices of the confirmed malicious worker. We have proven the effectiveness of our proposed method on the improvement of system performance in theoretical analysis. Intensive experiments show the accuracy of VAWS is over 97% and the overhead of computation is closed to the ideal value of 2 with the increasing of the number of map tasks in our scheme.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E97.D.721/_p

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@ARTICLE{e97-d_4_721,
author={Yan DING, Huaimin WANG, Lifeng WEI, Songzheng CHEN, Hongyi FU, Xinhai XU, },
journal={IEICE TRANSACTIONS on Information},
title={VAWS: Constructing Trusted Open Computing System of MapReduce with Verified Participants},
year={2014},
volume={E97-D},
number={4},
pages={721-732},
abstract={MapReduce is commonly used as a parallel massive data processing model. When deploying it as a service over the open systems, the computational integrity of the participants is becoming an important issue due to the untrustworthy workers. Current duplication-based solutions can effectively solve non-collusive attacks, yet most of them require a centralized worker to re-compute additional sampled tasks to defend collusive attacks, which makes the worker a bottleneck. In this paper, we try to explore a trusted worker scheduling framework, named VAWS, to detect collusive attackers and assure the integrity of data processing without extra re-computation. Based on the historical results of verification, we construct an Integrity Attestation Graph (IAG) in VAWS to identify malicious mappers and remove them from the framework. To further improve the efficiency of identification, a verification-couple selection method with the IAG guidance is introduced to detect the potential accomplices of the confirmed malicious worker. We have proven the effectiveness of our proposed method on the improvement of system performance in theoretical analysis. Intensive experiments show the accuracy of VAWS is over 97% and the overhead of computation is closed to the ideal value of 2 with the increasing of the number of map tasks in our scheme.},
keywords={},
doi={10.1587/transinf.E97.D.721},
ISSN={1745-1361},
month={April},}

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TY - JOUR
TI - VAWS: Constructing Trusted Open Computing System of MapReduce with Verified Participants
T2 - IEICE TRANSACTIONS on Information
SP - 721
EP - 732
AU - Yan DING
AU - Huaimin WANG
AU - Lifeng WEI
AU - Songzheng CHEN
AU - Hongyi FU
AU - Xinhai XU
PY - 2014
DO - 10.1587/transinf.E97.D.721
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2014
AB - MapReduce is commonly used as a parallel massive data processing model. When deploying it as a service over the open systems, the computational integrity of the participants is becoming an important issue due to the untrustworthy workers. Current duplication-based solutions can effectively solve non-collusive attacks, yet most of them require a centralized worker to re-compute additional sampled tasks to defend collusive attacks, which makes the worker a bottleneck. In this paper, we try to explore a trusted worker scheduling framework, named VAWS, to detect collusive attackers and assure the integrity of data processing without extra re-computation. Based on the historical results of verification, we construct an Integrity Attestation Graph (IAG) in VAWS to identify malicious mappers and remove them from the framework. To further improve the efficiency of identification, a verification-couple selection method with the IAG guidance is introduced to detect the potential accomplices of the confirmed malicious worker. We have proven the effectiveness of our proposed method on the improvement of system performance in theoretical analysis. Intensive experiments show the accuracy of VAWS is over 97% and the overhead of computation is closed to the ideal value of 2 with the increasing of the number of map tasks in our scheme.
ER -