Publications AS: Bibliography 2024 BibTeX
@inproceedings {INPROC-2024-03,
author = {Andrea Fieschi and Pascal Hirmer and Sachin Agrawal and Christoph Stach and Bernhard Mitschang},
title = {{HySAAD - A Hybrid Selection Approach for Anonymization by Design in the Automotive Domain}},
booktitle = {Proceedings of the 25th IEEE International Conference on Mobile Data Management (MDM 2024)},
editor = {Chiara Renso and Mahmoud Sakr and Walid G Aref and Ashley Song and Cheng Long},
address = {Los Alamitos, Washington, Tokyo},
publisher = {IEEE Computer Society Conference Publishing Services},
institution = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany},
pages = {203--210},
type = {Conference Paper},
month = {June},
year = {2024},
isbn = {979-8-3503-7455-1},
issn = {2375-0324},
doi = {10.1109/MDM61037.2024.00044},
keywords = {anonymization; connected vehicles; privacy protection; metrics},
language = {English},
cr-category = {K.4.1 Computers and Society Public Policy Issues},
contact = {Senden Sie eine E-Mail an \<andrea.fieschi@ipvs.uni-stuttgart.de\>.},
department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Applications of Parallel and Distributed Systems},
abstract = {The increasing connectivity and data exchange between vehicles and the cloud
have led to growing privacy concerns. To keep on gaining product insights
through data collection while guaranteeing privacy protection, an
anonymization-by-design approach should be used. A rising number of
anonymization methods, not limited to the automotive domain, can be found in
the literature and practice. The developers need support to select the suitable
anonymization technique. To this end, we make the following two contributions:
1) We apply our knowledge from the automotive domain to outline the usage of
qualitative metrics for anonymization techniques assessment; 2) We introduce
HySAAD, a hybrid selection approach for anonymization by design that leverages
this groundwork by recommending appropriate anonymization techniques for each
mobile data analytics use case based on both, qualitative (i.e., {\ss}oft``) metrics
and quantitative (i.e., ''hard``) metrics. Using a real-world use case from the
automotive, we demonstrate the applicability and effectiveness of HySAAD.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2024-03&engl=1}
}
@inproceedings {INPROC-2024-02,
author = {Yunxuan Li and Christoph Stach and Bernhard Mitschang},
title = {{PaDS: An adaptive and privacy-enabling Data Pipeline for Smart Cars}},
booktitle = {Proceedings of the 25th IEEE International Conference on Mobile Data Management (MDM 2024)},
editor = {Chiara Renso and Mahmoud Sakr and Walid G Aref and Kyoung-Sook Kim and Manos Papagelis and Dimitris Sacharidis},
address = {Los Alamitos, Washington, Tokyo},
publisher = {IEEE Computer Society Conference Publishing Services},
institution = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany},
pages = {41--50},
type = {Conference Paper},
month = {June},
year = {2024},
isbn = {979-8-3503-7455-1},
issn = {2375-0324},
doi = {10.1109/MDM61037.2024.00026},
keywords = {smart car; privacy-enabling data pipeline; datastream runtime adaptation; mobile data privacy management},
language = {English},
cr-category = {K.4.1 Computers and Society Public Policy Issues},
contact = {Senden Sie eine E-Mail an \<yunxuan.li@ipvs.uni-stuttgart.de\>.},
department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Applications of Parallel and Distributed Systems},
abstract = {The extensive use of onboard sensors in smart cars enables the collection,
processing, and dissemination of large amounts of mobile data containing
information about the vehicle, its driver, and even bystanders. Despite the
undoubted benefits of such smart cars, this leads to significant privacy
concerns. Due to their inherent mobility, the situation of smart cars changes
frequently, and with it, the appropriate measures to counteract the exposure of
private data. However, data management in such vehicles lacks sufficient
support for this privacy dynamism. We therefore introduce PaDS, a framework for
Privacy adaptive Data Stream. The focus of this paper is to enable adaptive
data processing within the vehicle data stream. With PaDS, Privacy-Enhancing
Technologies can be deployed dynamically in the data pipeline of a smart car
according to the current situation without user intervention. With a comparison
of state-of-the-art approaches, we demonstrate that our solution is very
efficient as it does not require a complete restart of the data pipeline.
Moreover, compared to a static approach, PaDS causes only minimal overhead
despite its dynamic adaptation of the data pipeline to react to changing
privacy requirements. This renders PaDS an effective privacy solution for smart
cars.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2024-02&engl=1}
}
@inproceedings {INPROC-2024-01,
author = {Dennis Przytarski and Christoph Stach and Bernhard Mitschang},
title = {{Assessing Data Layouts to Bring Storage Engine Functionality to Blockchain Technology}},
booktitle = {Proceedings of the 57th Hawaii International Conference on System Sciences (HICSS '24)},
editor = {Tung X. Bui},
publisher = {ScholarSpace},
institution = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany},
pages = {5091--5100},
type = {Conference Paper},
month = {January},
year = {2024},
isbn = {978-0-9981331-7-1},
keywords = {blockchain; storage engine; queries},
language = {English},
cr-category = {H.3.1 Content Analysis and Indexing,
H.3.2 Information Storage,
H.3.3 Information Search and Retrieval},
ee = {https://hdl.handle.net/10125/106995},
contact = {Senden Sie eine E-Mail an \<Christoph.Stach@ipvs.uni-stuttgart.de\>.},
department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Applications of Parallel and Distributed Systems},
abstract = {Nowdays, modern applications often use blockchains as a secure data store.
However, querying blockchain data is more challenging than querying
conventional databases due to blockchains being primarily designed for the
logging of asset transfers, such as cryptocurrencies, rather than storing and
reading generic data. To improve the experience of querying blockchain data and
make it comparable to querying conventional databases, new design approaches of
the storage engine for blockchain technology are required. An important aspect
is the data layout of a block, as it plays a crucial role in facilitating
reading of blockchain data. In this paper, we identify a suitable data layout
that provides the required query capabilities while preserving the key
properties of blockchain technology. Our goal is to overcome the limitations of
current data access models in blockchains, such as the reliance on auxiliary
data storages and error-prone smart contracts. To this end, we compare four
promising data layouts with data models derived from document, row, column, and
triple stores in terms of schema flexibility, read pattern generality, and
relational algebra suitability. We then assess the most suitable data layout
for blockchain technology.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2024-01&engl=1}
}
