by Yuriy Brun, Nenad Medvidovic
Abstract:
We present sTile, a technique for distributing trust-needing computation onto insecure networks, while providing probabilistic guarantees that malicious agents that compromise parts of the network cannot learn private data. With sTile, we explore the fundamental cost of achieving privacy through data distribution and bound how much less efficient a privacy-preserving system is than a non-private one. This paper focuses specifically on NP-complete problems and demonstrates how sTile-based systems can solve important real-world problems, such as protein folding, image recognition, and resource allocation. We present the algorithms involved in sTile and formally prove that sTile-based systems preserve privacy. We develop a reference sTile-based implementation and empirically evaluate it on several physical networks of varying sizes, including the globally distributed PlanetLab testbed. Our analysis demonstrates sTile's scalability and ability to handle varying network delay, as well as verifies that problems requiring privacy-preservation can be solved using sTile orders of magnitude faster than using today's state-of-the-art alternatives.
Citation:
Yuriy Brun and Nenad Medvidovic, Entrusting Private Computation and Data to Untrusted Networks, IEEE Transactions on Dependable and Secure Computing (TDSC), vol. 10, no. 4, July/August 2013, pp. 225–238.
Bibtex:
@article{Brun13tdsc,
title =
{\href{http://people.cs.umass.edu/brun/pubs/pubs/Brun13tdsc.pdf}{Entrusting
Private Computation and Data to Untrusted Networks}},
author = {Yuriy Brun and Nenad Medvidovic},
journal = {IEEE Transactions on Dependable and Secure Computing (TDSC)},
venue = {TDSC},
issn = {1545-5971},
year = {2013},
doi = {10.1109/TDSC.2013.13},
month = {July/August},
pages = {225--238},
volume = {10},
number = {4},
note = {\href{https://doi.org/10.1109/TDSC.2013.13}{DOI:
10.1109/TDSC.2013.13}},
abstract = {We present sTile, a technique for distributing trust-needing
computation onto insecure networks, while providing probabilistic guarantees
that malicious agents that compromise parts of the network cannot learn
private data. With sTile, we explore the fundamental cost of achieving privacy
through data distribution and bound how much less efficient a
privacy-preserving system is than a non-private one. This paper focuses
specifically on NP-complete problems and demonstrates how sTile-based systems
can solve important real-world problems, such as protein folding, image
recognition, and resource allocation. We present the algorithms involved in
sTile and formally prove that sTile-based systems preserve privacy. We develop
a reference sTile-based implementation and empirically evaluate it on several
physical networks of varying sizes, including the globally distributed
PlanetLab testbed. Our analysis demonstrates sTile's scalability and ability
to handle varying network delay, as well as verifies that problems requiring
privacy-preservation can be solved using sTile orders of magnitude faster than
using today's state-of-the-art alternatives.},
fundedBy = {NSF CNS-0937060 to the CRA for the CIFellows Project, NSF CCF-0905665,
NSF CCF-1117593, Infosys Technologies Ltd},
}