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SEMROD: Secure and Efficient MapReduce Over HybriD Clouds

Authors:

Kerim Yasin Oktay,

Sharad Mehrotra,

Vaibhav Khadilkar,

Murat KantarciogluAuthors Info & Claims

SIGMOD '15: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

Pages 153 - 166

https://doi.org/10.1145/2723372.2723741

Published: 27 May 2015 Publication History

Abstract

This paper describes SEMROD, a sensitive data aware MapReduce (MR) framework for hybrid clouds. SEMROD steers data and computation through public and private machines in such a way that no knowledge about sensitive data is leaked to public machines. For this purpose, SEMROD keeps trace of intermediate keys (generated during MR execution) that become sensitive, based on which it makes dynamic task scheduling decisions. SEMROD guarantees that adversaries viz. public machines) cannot gain any ``additional'' information about sensitive data from either the data stored on public machines or the communication between public and private machines during job execution. SEMROD extends naturally from a single MR job to multi-phase MR jobs that result, for instance, from compiling Hive queries into MR jobs. Using SEMROD, computation that may involve sensitive data can exploit public machines, thereby bringing significant performance benefits. Such computation would otherwise be restricted to only private clouds. Our experiments clearly demonstrate performance advantages to using SEMROD as compared with other secure alternatives, even when the percentage of sensitive data is as high as 50%.

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Cited By

Pappachan PZhang SHe XMehrotra S(2024)Preventing Inferences Through Data Dependencies on Sensitive DataIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.333663036:10(5308-5327)Online publication date: Oct-2024
https://doi.org/10.1109/TKDE.2023.3336630
Li XLi FGao M(2023)Flare: A Fast, Secure, and Memory-Efficient Distributed Analytics FrameworkProceedings of the VLDB Endowment10.14778/3583140.358315816:6(1439-1452)Online publication date: 20-Apr-2023
https://dl.acm.org/doi/10.14778/3583140.3583158
Azumah KMaciel PSørensen LKosta S(2023)Modeling and Simulating a Process Mining-Influenced Load-Balancer for the Hybrid CloudIEEE Transactions on Cloud Computing10.1109/TCC.2022.317766811:2(1999-2010)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TCC.2022.3177668
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Index Terms

SEMROD: Secure and Efficient MapReduce Over HybriD Clouds
1. Information systems

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cover image ACM Conferences

SIGMOD '15: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

May 2015

2110 pages

ISBN:9781450327589

DOI:10.1145/2723372

General Chair:
Timos Sellis
RMIT University, Australia
,
Program Chairs:
Susan B. Davidson
University of Pennsylvania, USA
,
Zack Ives
University of Pennsylvania, USA

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

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Published: 27 May 2015

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SIGMOD/PODS'15: International Conference on Management of Data

May 31 - June 4, 2015

Victoria, Melbourne, Australia

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SIGMOD '15 Paper Acceptance Rate 106 of 415 submissions, 26%;

Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

Pappachan PZhang SHe XMehrotra S(2024)Preventing Inferences Through Data Dependencies on Sensitive DataIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.333663036:10(5308-5327)Online publication date: Oct-2024
https://doi.org/10.1109/TKDE.2023.3336630
Li XLi FGao M(2023)Flare: A Fast, Secure, and Memory-Efficient Distributed Analytics FrameworkProceedings of the VLDB Endowment10.14778/3583140.358315816:6(1439-1452)Online publication date: 20-Apr-2023
https://dl.acm.org/doi/10.14778/3583140.3583158
Azumah KMaciel PSørensen LKosta S(2023)Modeling and Simulating a Process Mining-Influenced Load-Balancer for the Hybrid CloudIEEE Transactions on Cloud Computing10.1109/TCC.2022.317766811:2(1999-2010)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TCC.2022.3177668
Pappachan PZhang SHe XMehrotra S(2022)Don't be a tattle-taleProceedings of the VLDB Endowment10.14778/3551793.355180515:11(2437-2449)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.14778/3551793.3551805
Mehrotra SSharma SUllman JGhosh DGupta PMishra A(2020)PANDAACM Transactions on Management Information Systems10.1145/339752111:4(1-41)Online publication date: 12-Oct-2020
https://dl.acm.org/doi/10.1145/3397521
Sharma SBurtsev AMehrotra S(2020)Advances in Cryptography and Secure Hardware for Data Outsourcing2020 IEEE 36th International Conference on Data Engineering (ICDE)10.1109/ICDE48307.2020.00173(1798-1801)Online publication date: Apr-2020
https://doi.org/10.1109/ICDE48307.2020.00173
Kawamoto SKamidoi YWakabayashi S(2020)A Framework for Fast MapReduce Processing Considering Sensitive Data on Hybrid Clouds2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC48688.2020.00-67(1357-1362)Online publication date: Jul-2020
https://doi.org/10.1109/COMPSAC48688.2020.00-67
Gao YChen XDu X(2020)A Big Data Provenance Model for Data Security Supervision Based on PROV-DM ModelIEEE Access10.1109/ACCESS.2020.29758208(38742-38752)Online publication date: 2020
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Azumah KSørensen LMontella RKosta S(2020)Process mining‐constrained scheduling in the hybrid cloudConcurrency and Computation: Practice and Experience10.1002/cpe.602533:4Online publication date: 24-Sep-2020
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Revathi TMuneeswaran KBlessa Binolin Pepsi M(2019)YARNBig Data Processing With Hadoop10.4018/978-1-5225-3790-8.ch006(90-124)Online publication date: 2019
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