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  4. Clouseau: Blockchain-based Data Integrity for HDFS Clusters
 
conference paper

Clouseau: Blockchain-based Data Integrity for HDFS Clusters

Konsta, Alyzia
•
Mytilinis, Ioannis  
•
Doka, Katerina
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January 1, 2021
2021 Ieee 37Th International Conference On Data Engineering (Icde 2021)
37th IEEE International Conference on Data Engineering (IEEE ICDE)

As the volume of produced data is exponentially increasing, companies tend to rely on distributed systems to meet the surging demand for storage capacity. With the business workflows becoming more and more complex, such systems often consist of or are accessed by multiple independent, untrusted entities, which need to interact with shared data. In such scenarios, the potential conflicts of interest incentivize malicious parties to act in a dishonest way and tamper the data to their own benefit. The decentralized nature of the systems renders verifiable data integrity a strenuous but necessary task: The various parties should be able to audit changes and detect tampering when it happens.

In this work, we focus on HDFS, the most common storage substrate for Big Data analytics. HDFS is vulnerable to malicious users and participating nodes and does not provide a trustful lineage mechanism, thus jeopardizing the integrity of stored data and the credibility of extracted insights. As a remedy, we present Clouseau, a blockchain-based system that provides verifiable integrity over HDFS, while it does not incur significant overhead at the critical path of read/write operations. During the demonstration, the attendees will have the chance to interact with Clouseau, corrupt data themselves, and witness how Clouseau detects malicious actions.

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Type
conference paper
DOI
10.1109/ICDE51399.2021.00314
Web of Science ID

WOS:000687830800306

Author(s)
Konsta, Alyzia
Mytilinis, Ioannis  
Doka, Katerina
Niarchos, Sotiris
Koziris, Nectarios
Date Issued

2021-01-01

Publisher

IEEE COMPUTER SOC

Publisher place

Los Alamitos

Published in
2021 Ieee 37Th International Conference On Data Engineering (Icde 2021)
ISBN of the book

978-1-7281-9184-3

Series title/Series vol.

IEEE International Conference on Data Engineering

Start page

2725

End page

2728

Subjects

Computer Science, Information Systems

•

Computer Science, Theory & Methods

•

Computer Science

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
DIAS  
Event nameEvent placeEvent date
37th IEEE International Conference on Data Engineering (IEEE ICDE)

ELECTR NETWORK

Apr 19-22, 2021

Available on Infoscience
September 25, 2021
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/181663
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