PFC: Privacy preserving FPGA cloud - A case study of MapReduce

Lei Xu, Weidong Shi, Taeweon Suh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

Privacy is one of the critical concerns that hinder the adoption of public cloud. For storage, encryption can be used to protect user's data. But for outsourced data processing, for example MapReduce, there is no satisfying solution. Users have to trust the cloud service providers totally. In this work, we propose PFC, a FPGA cloud for privacy preserving computation in the public cloud environment. PFC leverages the security feature of the existing FPGAs originally designed for bitstream IP protection and proxy re-encryption for preserving user data privacy. In PFC, cloud service providers are not necessarily trusted, and during outsourced computation, user's data is protected by a data encryption key only accessible by trusted FPGA devices. As an important application of cloud computing, we apply PFC to the popular MapReduce programming model and extend the FPGA based MapReduce pipeline with privacy protection capabilities. Proxy re-encryption is employed to support dynamic allocations of trusted FPGA devices as mappers and reducers. Finally, we conduct evaluation to demonstrate the effectiveness of PFC.

Original languageEnglish
Title of host publicationIEEE International Conference on Cloud Computing, CLOUD
PublisherIEEE Computer Society
Pages280-287
Number of pages8
ISBN (Print)9781479950638
DOIs
Publication statusPublished - 2014 Jan 1
Event7th IEEE International Conference on Cloud Computing, CLOUD 2014 - Anchorage, United States
Duration: 2014 Jun 272014 Jul 2

Other

Other7th IEEE International Conference on Cloud Computing, CLOUD 2014
CountryUnited States
CityAnchorage
Period14/6/2714/7/2

Fingerprint

Field programmable gate arrays (FPGA)
Cryptography
Data privacy
Cloud computing
Pipelines

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Software

Cite this

Xu, L., Shi, W., & Suh, T. (2014). PFC: Privacy preserving FPGA cloud - A case study of MapReduce. In IEEE International Conference on Cloud Computing, CLOUD (pp. 280-287). [6973752] IEEE Computer Society. https://doi.org/10.1109/CLOUD.2014.46

PFC : Privacy preserving FPGA cloud - A case study of MapReduce. / Xu, Lei; Shi, Weidong; Suh, Taeweon.

IEEE International Conference on Cloud Computing, CLOUD. IEEE Computer Society, 2014. p. 280-287 6973752.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Xu, L, Shi, W & Suh, T 2014, PFC: Privacy preserving FPGA cloud - A case study of MapReduce. in IEEE International Conference on Cloud Computing, CLOUD., 6973752, IEEE Computer Society, pp. 280-287, 7th IEEE International Conference on Cloud Computing, CLOUD 2014, Anchorage, United States, 14/6/27. https://doi.org/10.1109/CLOUD.2014.46
Xu L, Shi W, Suh T. PFC: Privacy preserving FPGA cloud - A case study of MapReduce. In IEEE International Conference on Cloud Computing, CLOUD. IEEE Computer Society. 2014. p. 280-287. 6973752 https://doi.org/10.1109/CLOUD.2014.46
Xu, Lei ; Shi, Weidong ; Suh, Taeweon. / PFC : Privacy preserving FPGA cloud - A case study of MapReduce. IEEE International Conference on Cloud Computing, CLOUD. IEEE Computer Society, 2014. pp. 280-287
@inproceedings{0b273092e056431a807bf6522dd414fb,
title = "PFC: Privacy preserving FPGA cloud - A case study of MapReduce",
abstract = "Privacy is one of the critical concerns that hinder the adoption of public cloud. For storage, encryption can be used to protect user's data. But for outsourced data processing, for example MapReduce, there is no satisfying solution. Users have to trust the cloud service providers totally. In this work, we propose PFC, a FPGA cloud for privacy preserving computation in the public cloud environment. PFC leverages the security feature of the existing FPGAs originally designed for bitstream IP protection and proxy re-encryption for preserving user data privacy. In PFC, cloud service providers are not necessarily trusted, and during outsourced computation, user's data is protected by a data encryption key only accessible by trusted FPGA devices. As an important application of cloud computing, we apply PFC to the popular MapReduce programming model and extend the FPGA based MapReduce pipeline with privacy protection capabilities. Proxy re-encryption is employed to support dynamic allocations of trusted FPGA devices as mappers and reducers. Finally, we conduct evaluation to demonstrate the effectiveness of PFC.",
keywords = "Cloud computing, Data security, FPGA, MapReduce",
author = "Lei Xu and Weidong Shi and Taeweon Suh",
year = "2014",
month = "1",
day = "1",
doi = "10.1109/CLOUD.2014.46",
language = "English",
isbn = "9781479950638",
pages = "280--287",
booktitle = "IEEE International Conference on Cloud Computing, CLOUD",
publisher = "IEEE Computer Society",

}

TY - GEN

T1 - PFC

T2 - Privacy preserving FPGA cloud - A case study of MapReduce

AU - Xu, Lei

AU - Shi, Weidong

AU - Suh, Taeweon

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Privacy is one of the critical concerns that hinder the adoption of public cloud. For storage, encryption can be used to protect user's data. But for outsourced data processing, for example MapReduce, there is no satisfying solution. Users have to trust the cloud service providers totally. In this work, we propose PFC, a FPGA cloud for privacy preserving computation in the public cloud environment. PFC leverages the security feature of the existing FPGAs originally designed for bitstream IP protection and proxy re-encryption for preserving user data privacy. In PFC, cloud service providers are not necessarily trusted, and during outsourced computation, user's data is protected by a data encryption key only accessible by trusted FPGA devices. As an important application of cloud computing, we apply PFC to the popular MapReduce programming model and extend the FPGA based MapReduce pipeline with privacy protection capabilities. Proxy re-encryption is employed to support dynamic allocations of trusted FPGA devices as mappers and reducers. Finally, we conduct evaluation to demonstrate the effectiveness of PFC.

AB - Privacy is one of the critical concerns that hinder the adoption of public cloud. For storage, encryption can be used to protect user's data. But for outsourced data processing, for example MapReduce, there is no satisfying solution. Users have to trust the cloud service providers totally. In this work, we propose PFC, a FPGA cloud for privacy preserving computation in the public cloud environment. PFC leverages the security feature of the existing FPGAs originally designed for bitstream IP protection and proxy re-encryption for preserving user data privacy. In PFC, cloud service providers are not necessarily trusted, and during outsourced computation, user's data is protected by a data encryption key only accessible by trusted FPGA devices. As an important application of cloud computing, we apply PFC to the popular MapReduce programming model and extend the FPGA based MapReduce pipeline with privacy protection capabilities. Proxy re-encryption is employed to support dynamic allocations of trusted FPGA devices as mappers and reducers. Finally, we conduct evaluation to demonstrate the effectiveness of PFC.

KW - Cloud computing

KW - Data security

KW - FPGA

KW - MapReduce

UR - http://www.scopus.com/inward/record.url?scp=84919785584&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84919785584&partnerID=8YFLogxK

U2 - 10.1109/CLOUD.2014.46

DO - 10.1109/CLOUD.2014.46

M3 - Conference contribution

AN - SCOPUS:84919785584

SN - 9781479950638

SP - 280

EP - 287

BT - IEEE International Conference on Cloud Computing, CLOUD

PB - IEEE Computer Society

ER -