SecureMR: A Service Integrity Assurance Framework for MapReduce

Wei, Wei; Du, Juan; Yu, Ting; Gu, Xiaohui

doi:10.1109/acsac.2009.17

Cited by 107 publications

(89 citation statements)

References 19 publications

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“…Considering the untrustable SPs, Chu Huang et al proposed a watermark injection method to verify if the computation is completed correctly [19]. For the untrustable participating nodes in an open MapReduce environment, Wei Wei et al proposed an integrity protection mechanism called SecureMR, which uses two-copy replication to verify the result in the map phase [9]. Yongzhi Wang and his colleagues introduced the verifier role in the MapReduce computing model [10], which samples and re-computes the results passed the replication verification, to defend collusion attack.…”

Section: Related Workmentioning

confidence: 99%

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

Ding

Wang

Wei

et al. 2014

IEICE Trans. Inf. & Syst.

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SUMMARYMapReduce 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. key words: result verification, computational integrity, MapReduce, open system, integrity attestation graph

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Section: Related Workmentioning

confidence: 99%

“…Current studies in this area are mainly based on the redundant computing principal, that is to dispatch task to multiple replicas, and vote on results to obtain correct ones [9]. Such scheme, however, is insufficient to effectively prevent collusive attacks.…”

Section: Introductionmentioning

confidence: 99%

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

Ding

Wang

Wei

et al. 2014

IEICE Trans. Inf. & Syst.

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“…Their work focuses on protecting privacy issues of untrusted code of data-mining and data-analysis algorithms executed on MapReduce. Wei et al [6] proposed a secure scheme called SecureMR which aims at protecting the computation integrity issue of MapReduce. SecureMR detects misbehavior of mappers by sending same tasks to multiple mappers, and check consistency of results.…”

Section: Related Workmentioning

confidence: 99%

Towards Trusted Services: Result Verification Schemes for MapReduce

Huang

Zhu

2012

2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (Ccgrid 2012)

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Abstract-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.

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“…Integrity verification: In other directions, [23] replicates some map/reduce tasks and assign them to different mappers/reducers to validate the integrity of map/reduce tasks. Any inconsistent intermediate results from those mappers/reducers reveal attacks.…”

Section: Security In Mapreducementioning

confidence: 99%

TrustedMR: A Trusted MapReduce System Based on Tamper Resistance Hardware

Nguyen

Pelletier

2015

Lecture Notes in Computer Science

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Abstract. With scalability, fault tolerance, ease of programming, and flexibility, MapReduce has gained many attractions for large-scale data processing. However, despite its merits, MapReduce does not focus on the problem of data privacy, especially when processing sensitive data, such as personal data, on untrusted infrastructure. In this paper, we investigate a scenario based on the Trusted Cells paradigm : a user stores his personal data in a local secure data store and wants to process this data using MapReduce on a third party infrastructure, on which secure devices are also connected. The main contribution of the paper is to present TrustedMR, a trusted MapReduce system with high security assurance provided by tamper-resistant hardware, to enforce the security aspect of the MapReduce. Thanks to TrustedMR, encrypted data can then be processed by untrusted computing nodes without any modification to the existing MapReduce framework and code. Our evaluation shows that the performance overhead of TrustedMR is limited to few percents, compared to an original MapReduce framework that handles cleartexts.

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SecureMR: A Service Integrity Assurance Framework for MapReduce

Cited by 107 publications

References 19 publications

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

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

Towards Trusted Services: Result Verification Schemes for MapReduce

TrustedMR: A Trusted MapReduce System Based on Tamper Resistance Hardware

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