This file was created by the TYPO3 extension
bib
--- Timezone: CEST
Creation date: 2024-07-04
Creation time: 05-28-32
--- Number of references
3
incollection
2017-cps-henze-network
Network Security and Privacy for Cyber-Physical Systems
2017
11
13
25-56
sensorcloud,ipacs
Song, Houbing and Fink, Glenn A. and Jeschke, Sabina
Wiley-IEEE Press
First
2
Security and Privacy in Cyber-Physical Systems: Foundations, Principles and Applications
en
978-1-119-22604-8
10.1002/9781119226079.ch2
1
MartinHenze
JensHiller
RenéHummen
RomanMatzutt
KlausWehrle
Jan HenrikZiegeldorf
article
2017-ziegeldorf-bmcmedgenomics-bloom
BLOOM: BLoom filter based Oblivious Outsourced Matchings
BMC Medical Genomics
2017
7
26
10
Suppl 2
29-42
Whole genome sequencing has become fast, accurate, and cheap, paving the way towards the large-scale collection and processing of human genome data. Unfortunately, this dawning genome era does not only promise tremendous advances in biomedical research but also causes unprecedented privacy risks for the many. Handling storage and processing of large genome datasets through cloud services greatly aggravates these concerns. Current research efforts thus investigate the use of strong cryptographic methods and protocols to implement privacy-preserving genomic computations. We propose FHE-Bloom and PHE-Bloom, two efficient approaches for genetic disease testing using homomorphically encrypted Bloom filters. Both approaches allow the data owner to securely outsource storage and computation to an untrusted cloud. FHE-Bloom is fully secure in the semi-honest model while PHE-Bloom slightly relaxes security guarantees in a trade-off for highly improved performance. We implement and evaluate both approaches on a large dataset of up to 50 patient genomes each with up to 1000000 variations (single nucleotide polymorphisms). For both implementations, overheads scale linearly in the number of patients and variations, while PHE-Bloom is faster by at least three orders of magnitude. For example, testing disease susceptibility of 50 patients with 100000 variations requires only a total of 308.31 s (σ=8.73 s) with our first approach and a mere 0.07 s (σ=0.00 s) with the second. We additionally discuss security guarantees of both approaches and their limitations as well as possible extensions towards more complex query types, e.g., fuzzy or range queries. Both approaches handle practical problem sizes efficiently and are easily parallelized to scale with the elastic resources available in the cloud. The fully homomorphic scheme, FHE-Bloom, realizes a comprehensive outsourcing to the cloud, while the partially homomorphic scheme, PHE-Bloom, trades a slight relaxation of security guarantees against performance improvements by at least three orders of magnitude.
Proceedings of the 5th iDASH Privacy and Security Workshop 2016
Secure outsourcing; Homomorphic encryption; Bloom filters
sscilops; mynedata; rfc
https://www.comsys.rwth-aachen.de/fileadmin/papers/2017/2017-ziegeldorf-bmcmedgenomics-bloom.pdf
Online
BioMed Central
Chicago, IL, USA
November 11, 2016
en
1755-8794
10.1186/s12920-017-0277-y
1
Jan HenrikZiegeldorf
JanPennekamp
DavidHellmanns
FelixSchwinger
IkeKunze
MartinHenze
JensHiller
RomanMatzutt
KlausWehrle
inproceedings
2017-henze-ic2e-prada
Practical Data Compliance for Cloud Storage
2017
4
4
252-258
ssiclops, ipacs
https://www.comsys.rwth-aachen.de/fileadmin/papers/2017/2017-henze-ic2e-prada.pdf
Online
IEEE
Proceedings of the 2017 IEEE International Conference on Cloud Engineering (IC2E 2017), Vancouver, BC, Canada
en
978-1-5090-5817-4
10.1109/IC2E.2017.32
1
MartinHenze
RomanMatzutt
JensHiller
ErikMühmer
Jan HenrikZiegeldorf
Johannesvan der Giet
KlausWehrle