Towards Trusted Services: Result Verification Schemes for MapReduce

Huang, Chih‐Cheng; Zhu, Sencun; Wu, Dinghao

doi:10.1109/ccgrid.2012.77

Cited by 23 publications

(11 citation statements)

References 10 publications

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“…Apache Accumulo is built on top of Apache Hadoop, Zookeeper, and Thrift. [3] M 8 Apache Sentry [4] S Apache Ranger [4] M Project Rhino [5] S Apache Accumulo [6] S Airavat [92] S Khaled et al [57] Vigiles [109] GuardMR [107] G-Hadoop [135] SecDM [97] M iBigTable [119] Lin et al [64] S 9 10 SAPSC [96] M ClusterBFT [99] S, H 11 Moca et al [76] SecureMR [118] S AccountableMR [123] S VIAF [115] S CCMR [116] M IntegrityMR [117] M VAWS [39] S Hatman [58] S 12 TrustMR [108] S TS-TRV [38] S Log-based [125] S Watermarking based [53,37] S Accountable MapReduce (RBAC) [110] S Airavat. Airavat [92] is a system that provides mandatory access control together with differential privacy for data protection.…”

Section: Authentication Authorization and Access Control Based Apprmentioning

confidence: 99%

Security and privacy aspects in MapReduce on clouds: A survey

Derbeko

Dolev

Gudes

et al. 2016

Computer Science Review

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MapReduce is a programming system for distributed processing large-scale data in an efficient and fault tolerant manner on a private, public, or hybrid cloud. MapReduce is extensively used daily around the world as an efficient distributed computation tool for a large class of problems, e.g., search, clustering, log analysis, different types of join operations, matrix multiplication, pattern matching, and analysis of social networks. Security and privacy of data and MapReduce computations are essential concerns when a MapReduce computation is executed in public or hybrid clouds. In order to execute a MapReduce job in public and hybrid clouds, authentication of mappers-reducers, confidentiality of data-computations, integrity of data-computations, and correctness-freshness of the outputs are required. Satisfying these requirements shield the operation from several types of attacks on data and MapReduce computations. In this paper, we investigate and discuss security and privacy challenges and requirements, considering a variety of adversarial capabilities, and characteristics in the scope of MapReduce. We also provide a review of existing security and privacy protocols for MapReduce and discuss their overhead issues.

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Section: Authentication Authorization and Access Control Based Apprmentioning

confidence: 99%

Security and privacy aspects in MapReduce on clouds: A survey

Derbeko

Dolev

Gudes

et al. 2016

Computer Science Review

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“…The result verification schemes for MapReduce proposed in [30] are based on watermark injection and random sampling methods. These schemes can efficiently detect cheating behavior of MapReduce computing in the context of text processing problems, but have limited to the potential security risks from lazy or malicious servers involved in a MapReduce task.…”

Section: Related Workmentioning

confidence: 99%

Result verification mechanism for MapReduce computation integrity in cloud computing

Bendahmane

Essaaidi

Moussaoui

et al. 2012

2012 IEEE International Conference on Complex Systems (ICCS)

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MapReduce is becoming a powerful parallel data processing model and is adopted by many cloud services providers to build cloud computing framework. However, in public cloud systems, several service providers may come from different administration domain out of the user control and may be untrustworthy. Hence, security of MapReduce computation is essential in public cloud systems. Additionally, MapReduce dataprocessing services are long-running, which increases the possibility that an attacker is able to compromise some workers and make them misbehave to corrupt the integrity of all computations allocated to these workers. Thus, the computation integrity is a major concern for Mapreduce user in public cloud environment. In this paper, we propose a new mechanism to ensure the computation integrity of MapReduce in public cloud computing environment. By using replication-based voting method and reputation-based trust management system, our mechanism can efficiently detect both collusive and non-collusive malicious workers and guarantee high computation accuracy with an acceptable overhead.

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“…Common approaches and techniques range from replication scheme [5], [6], crypto-based attestation protocols [4], probabilistic verification methods [6], [7], to dynamic execution traces analysis [8]. In order to obtain strong security guarantees, most of the approaches require modification of original MapReduce work flow by adding extra computation due to either cryptographic attestation or repeated MapReduce tasks.…”

Section: Introductionmentioning

confidence: 99%

Towards Provenance-Based Anomaly Detection in MapReduce

Liao

Squicciarini

2015

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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MapReduce enables parallel and distributed processing of vast amount of data on a cluster of machines. However, such computing paradigm is subject to threats posed by malicious and cheating nodes or compromised user submitted code that could tamper data and computation since users maintain little control as the computation is carried out in a distributed fashion. In this paper, we focus on the analysis and detection of anomalies during the process of MapReduce computation. Accordingly, we develop a computational provenance system that captures provenance data related to MapReduce computation within the MapReduce framework in Hadoop. In particular, we identify a set of invariants against aggregated provenance information, which are later analyzed to uncover anomalies indicating possible tampering of data and computation. We conduct a series of experiments to show the efficiency and effectiveness of our proposed provenance system. 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing 978-1-4799-8006-2/15 $31.00

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Towards Trusted Services: Result Verification Schemes for MapReduce

Cited by 23 publications

References 10 publications

Security and privacy aspects in MapReduce on clouds: A survey

Security and privacy aspects in MapReduce on clouds: A survey

Result verification mechanism for MapReduce computation integrity in cloud computing

Towards Provenance-Based Anomaly Detection in MapReduce

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