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--- Timezone: CEST
Creation date: 2023-10-02
Creation time: 23-03-03
--- Number of references
9
article
2017-pennekamp-pmc-survey
A Survey on the Evolution of Privacy Enforcement on Smartphones and the Road Ahead
Pervasive and Mobile Computing
2017
12
42
58-76
With the increasing proliferation of smartphones, enforcing privacy of smartphone users becomes evermore important. Nowadays, one of the major privacy challenges is the tremendous amount of permissions requested by applications, which can significantly invade users' privacy, often without their knowledge. In this paper, we provide a comprehensive review of approaches that can be used to report on applications' permission usage, tune permission access, contain sensitive information, and nudge users towards more privacy-conscious behavior. We discuss key shortcomings of privacy enforcement on smartphones so far and identify suitable actions for the future.
Smartphones; Permission Granting; Privacy; Nudging
trinics
https://www.comsys.rwth-aachen.de/fileadmin/papers/2017/2017-pennekamp-pmc-survey.pdf
Online
Elsevier
en
1574-1192
10.1016/j.pmcj.2017.09.005
1
JanPennekamp
MartinHenze
KlausWehrle
inproceedings
2017-poormohammady
Dynamic Algorithm Selection for the Logic of Tasks in IoT Stream Processing Systems
13th International Conference on Network and Service Management
2017
11
26
Online
IEEE
13th International Conference on Network and Service Management, Tokyo, Japan
en
10.23919/CNSM.2017.8256009
1
EhsanPoormohammady
Jens HelgeReelfs
MirkoStoffers
KlausWehrle
ApostolosPapageorgiou
inproceedings
2017-henze-mobiquitous-cloudanalyzer
CloudAnalyzer: Uncovering the Cloud Usage of Mobile Apps
2017
11
7
262-271
Developers of smartphone apps increasingly rely on cloud services for ready-made functionalities, e.g., to track app usage, to store data, or to integrate social networks. At the same time, mobile apps have access to various private information, ranging from users' contact lists to their precise locations. As a result, app deployment models and data flows have become too complex and entangled for users to understand. We present CloudAnalyzer, a transparency technology that reveals the cloud usage of smartphone apps and hence provides users with the means to reclaim informational self-determination. We apply CloudAnalyzer to study the cloud exposure of 29 volunteers over the course of 19 days. In addition, we analyze the cloud usage of the 5000 most accessed mobile websites as well as 500 popular apps from five different countries. Our results reveal an excessive exposure to cloud services: 90 % of apps use cloud services and 36 % of apps used by volunteers solely communicate with cloud services. Given the information provided by CloudAnalyzer, users can critically review the cloud usage of their apps.
Privacy; Smartphones; Cloud Computing; Traffic Analysis
trinics
https://www.comsys.rwth-aachen.de/fileadmin/papers/2017/2017-henze-mobiquitous-cloudanalyzer.pdf
Online
ACM
Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous '17), November 7-10, 2017, Melbourne, VIC, Australia
Melbourne, VIC, Australia
November 7-10, 2017
en
978-1-4503-5368-7
10.1145/3144457.3144471
1
MartinHenze
JanPennekamp
DavidHellmanns
ErikMühmer
Jan HenrikZiegeldorf
ArthurDrichel
KlausWehrle
inproceedings
2017-panchenko-wpes-fingerprinting
Analysis of Fingerprinting Techniques for Tor Hidden Services
2017
10
30
https://www.comsys.rwth-aachen.de/fileadmin/papers/2017/2017-panchenko-wpes-fingerprinting.pdf
Online
ACM
Proceedings of the 16th Workshop on Privacy in the Electronic Society (WPES), co-located with the 24th ACM Conference on Computer and Communications Security (CCS), Dallas, TX, USA
en
978-1-4503-5175-1
10.1145/3139550.3139564
1
AndriyPanchenko
AsyaMitseva
MartinHenze
FabianLanze
KlausWehrle
ThomasEngel
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
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
article
dombrowski-vdi
Funktechnologien für Industrie 4.0
VDE Positionspapier
2017
6
1
VDE - Verband der Elektrotechnik, Elektronik, Informationstechnik e.V.
Stresemannallee 15, 60596 Frankfurt am Main, Germany
IsmetAktas
AlexanderBentkus
FlorianBonanati
ArminDekorsy
ChristianDombrowski
MichaelDoubrava
AliGolestani
FrankHofmann
MikeHeidrich
StefanHiensch
RüdigerKays
MichaelMeyer
AndreasMüller
Stephanten Brink
NedaPetreska
MilanPopovic
LutzRauchhaupt
AhmadSaad
HansSchotten
ChristophWöste
IngoWolff
inproceedings
2017-serror-ew-koi
From Radio Design to System Evaluations for Ultra-Reliable and Low-Latency Communication
2017
5
17
koi
https://www.comsys.rwth-aachen.de/fileadmin/papers/2017/2017-serror-radio-design-ew17.pdf
IEEE
Proc. of 23rd European Wireless Conference (EW17), Dresden, Germany
Dresden, Germany
Proc. of 23rd European Wireless Conference (EW17)
17.-19. May 2017
1
Shehzad AliAshraf
Y.-P. EricWang
SamehEldessoki
BerndHolfeld
DonaldParruca
MartinSerror
JamesGross
proceedings
2017-serror-netsys-industrial
Demo: A Realistic Use-case for Wireless Industrial Automation and Control
2017
3
16
koi
https://www.comsys.rwth-aachen.de/fileadmin/papers/2017/Ansari_et_al_Wireless_Industrial_Automation_Demo_NetSys_2017.pdf
IEEE
Göttingen, Germany
International Conference on Networked Systems (NetSys 2017)
10.1109/NetSys.2017.7931496
1
JunaidAnsari
IsmetAktas
ChristianBrecher
ChristophPallasch
NicolaiHoffmann
MarkusObdenbusch
MartinSerror
KlausWehrle
JamesGross
phdthesis
2017-parruca-phdthesis
Stochastic Optimization in OFDMA/LTE Networks
2017
RWTH Aachen University
DonaldParruca