An Efficient Approach to Improve Security for MapReduce Computation in Cloud System

Bendahmane, Ahmed; Bennasar, Hanane; Essaaidi, Mohamed

doi:10.1145/3230905.3230954

Cited by 2 publications

(2 citation statements)

References 30 publications

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“…A new technique is proposed based on reputationthreshold value using the method that is called voting [13]. In this technique, every task will be repeated and executed on various workers, and their outputs are gathered into sets based on outputs value.…”

Section: F Results Verification Mechanismmentioning

confidence: 99%

Survey of Computation Integrity Methods For Big Data

aly

Atwa²,

Mousa

2021

IJCI. International Journal of Computers and Information

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Nowadays, big data becomes widespread. Big data has great value, but it faces many challenges. One of these challenges is security. Many classic security techniques exist, but these mechanisms are not appropriate for big data security. To secure big data, it is necessary to secure many aspects such as infrastructure, data privacy, data management, and integrity and reactive. Securing computations in distributed programming frameworks and protecting non-relational data stores are two requirements for infrastructure protection. This survey will highlight securing MapReduce as one of the most popular distributed programming frameworks. Security of MapReduce computation is an important consideration when a MapReduce computation is performed on a public or hybrid cloud. When a MapReduce job is executed on public cloud or hybrid cloud, an integrity check of its result is required. In this survey, a set of previous techniques that check the result integrity of MapReduce will be explained. In addition to discussion of the advantages and disadvantages of each technique.

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Section: F Results Verification Mechanismmentioning

confidence: 99%

Survey of Computation Integrity Methods For Big Data

aly

Atwa²,

Mousa

2021

IJCI. International Journal of Computers and Information

View full text Add to dashboard Cite

show abstract

“…Generating generic spotter jobs efficiently that cannot easily be distinguished from actual tasks is still an open problem. Nevertheless, there are techniques for efficiently generating application-specific spotter jobs, such as ringer schemes for the inversion of one-way functions [62] and for map-reduce workloads [63].…”

Section: Rejected Resultsmentioning

confidence: 99%

A Blockchain-based Decentralized Electronic Marketplace for Computing Resources

et al. 2020

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We propose a framework for building a decentralized electronic marketplace for computing resources. The idea is that anyone with spare capacities can offer them on this marketplace, opening up the cloud computing market to smaller players, thus creating a more competitive environment compared to today’s market consisting of a few large providers. Trust is a crucial component in making an anonymized decentralized marketplace a reality. We develop protocols that enable participants to interact with each other in a fair way and show how these protocols can be implemented using smart contracts and blockchains. We discuss and evaluate our framework not only from a technical point of view, but also look at the wider context in terms of fair interactions and legal implications.

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An Efficient Approach to Improve Security for MapReduce Computation in Cloud System

Cited by 2 publications

References 30 publications

Survey of Computation Integrity Methods For Big Data

Survey of Computation Integrity Methods For Big Data

A Blockchain-based Decentralized Electronic Marketplace for Computing Resources

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