Keeping Data Private while Computing in the Cloud
by Yuriy Brun, Nenad Medvidovic
Abstract:
The cloud offers unprecedented access to computation. However, ensuring the privacy of that computation remains a significant challenge. In this paper, we address the problem of distributing computation onto the cloud in a way that preserves the privacy of the computation's data even from the cloud nodes themselves. The approach, called sTile, separates the computation into small subcomputations and distributes them in a way that makes it prohibitively hard to reconstruct the data. We evaluate sTile theoretically and empirically: First, we formally prove that sTile systems preserve privacy. Second, we deploy a prototype implementation on three different networks, including the globally-distributed PlanetLab testbed, to show that sTile is robust to network delay and efficient enough to significantly outperform existing privacy-preserving approaches.
Citation:
Yuriy Brun and Nenad Medvidovic, Keeping Data Private while Computing in the Cloud, in Proceedings of the 5th International Conference on Cloud Computing (CLOUD), 2012, pp. 285–294.
Related:
Previous versions appeared as University of Southern California, Center for Software Engineering technical reports USC-CSSE-2007-714 and USC-CSSE-2008-819.
Bibtex:
@inproceedings{Brun12cloud,
  author = {Yuriy Brun and Nenad Medvidovic},
  title =
  {\href{http://people.cs.umass.edu/brun/pubs/pubs/Brun12cloud.pdf}{Keeping Data
  Private while Computing in the Cloud}},
  booktitle = {Proceedings of the 5th International Conference on Cloud
  Computing (CLOUD)},
  venue = {CLOUD},
  month = {June},
  date = {24--29},
  year = {2012},
  pages = {285--294},  
  address = {Honolulu, HI, USA},
  doi = {10.1109/CLOUD.2012.126},
  accept = {$\frac{48}{282} \approx 17\%$},

  note = {Previous versions appeared as University of Southern California,
  Center for Software Engineering technical reports USC-CSSE-2007-714 and
  USC-CSSE-2008-819. \href{http://dx.doi.org/10.1109/CLOUD.2012.126}{DOI:
  10.1109/CLOUD.2012.126}},

  previous = {Previous versions appeared as University of Southern California,
  Center for Software Engineering technical reports USC-CSSE-2007-714 and
  USC-CSSE-2008-819.},

  abstract = {The cloud offers unprecedented access to computation. However,
  ensuring the privacy of that computation remains a significant challenge. In
  this paper, we address the problem of distributing computation onto the
  cloud in a way that preserves the privacy of the computation's data even
  from the cloud nodes themselves. The approach, called sTile, separates the
  computation into small subcomputations and distributes them in a way that
  makes it prohibitively hard to reconstruct the data. We evaluate sTile
  theoretically and empirically: First, we formally prove that sTile systems
  preserve privacy. Second, we deploy a prototype implementation on three
  different networks, including the globally-distributed PlanetLab testbed, to
  show that sTile is robust to network delay and efficient enough to
  significantly outperform existing privacy-preserving approaches.},
}