Publications

[ Article ]
[ Inproceedings ]
[ Mastersthesis ]

Article[ to top ]

People Detection with Depth Silhouettes and Convolutional Neural Networks on a Mobile Robot. F. Spiess; L. Reinhart; N. Strobel; D. Kaiser; S. Kounev; T. Kaupp; in Journal of Image and Graphics, (J. of Image; Graphics, Hrsg.) (2021). 9(4) 135–139.
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
@article{spiess2021people, author = {Spiess, Florian and Reinhart, Lucas and Strobel, Norbert and Kaiser, Dennis and Kounev, Samuel and Kaupp, Tobias}, editor = {of Image, Journal and Graphics}, journal = {Journal of Image and Graphics}, keywords = {t_journalmagazine}, month = 12, number = 4, pages = {135-139}, title = {People Detection with Depth Silhouettes and Convolutional Neural Networks on a Mobile Robot}, volume = 9, year = 2021 }

%0 Journal Article %1 spiess2021people %A Spiess, Florian %A Reinhart, Lucas %A Strobel, Norbert %A Kaiser, Dennis %A Kounev, Samuel %A Kaupp, Tobias %D 2021 %E of Image, Journal %E Graphics, %J Journal of Image and Graphics %N 4 %P 135-139 %T People Detection with Depth Silhouettes and Convolutional Neural Networks on a Mobile Robot %U http://www.joig.net/index.php?m=content&c=index&a=show&catid=72&id=277 %V 9
{Towards Self-Aware Multirotor Formations}. D. Kaiser; V. Lesch; J. Rothe; M. Strohmeier; F. Spiess; C. Krupitzer; S. Montenegro; S. Kounev; in Computers (2020). 9(7)

Special Issue on Self-Aware Computing
- [ BibTeX ]
- [ EndNote ]
- [ Download ]
@article{Kaiser2020Towards, author = {Kaiser, Dennis and Lesch, Veronika and Rothe, Julian and Strohmeier, Michael and Spiess, Florian and Krupitzer, Christian and Montenegro, Sergio and Kounev, Samuel}, journal = {Computers}, keywords = {Intelligent_Transportation_Systems}, month = {02}, note = {Special Issue on Self-Aware Computing}, number = 7, publisher = {MDPI}, title = {{Towards Self-Aware Multirotor Formations}}, volume = 9, year = 2020 }

%0 Journal Article %1 Kaiser2020Towards %A Kaiser, Dennis %A Lesch, Veronika %A Rothe, Julian %A Strohmeier, Michael %A Spiess, Florian %A Krupitzer, Christian %A Montenegro, Sergio %A Kounev, Samuel %D 2020 %I MDPI %J Computers %N 7 %T {Towards Self-Aware Multirotor Formations} %V 9

Inproceedings[ to top ]

Performance Impact Analysis of Homomorphic Encryption: A Case Study Using Linear Regression as an Example. T. Prantl; S. Engel; L. Horn; D. Kaiser; L. Iffländer; A. Bauer; C. Krupitzer; S. Kounev; in Information Security Practice and Experience, W. Meng, Z. Yan, V. Piuri (Hrsg.) (2023). 284–298.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
In recent years, the trend has increasingly been to store and process data in the cloud. However, this is based on the premise that cloud providers treat the data in a trustworthy manner. One way of using the data in the cloud without the provider having access to it is homomorphic encryption. However, since this encryption has only recently become practicable, analysis of its for practical applications is still in its infancy. Therefore, we investigate the performance of homomorphic encryption using a real-world application, namely linear regression. Our main finding is that although the homomorphic computation of linear regression is in the range of minutes and thus slower than in the non-homomorphic case, linear regression can be computed homomorphic and is therefore suitable for use cases where data security is the top priority.

@inproceedings{PrEnHoDeIfBaKrKo_ISPEC_De_Bello, abstract = {In recent years, the trend has increasingly been to store and process data in the cloud. However, this is based on the premise that cloud providers treat the data in a trustworthy manner. One way of using the data in the cloud without the provider having access to it is homomorphic encryption. However, since this encryption has only recently become practicable, analysis of its for practical applications is still in its infancy. Therefore, we investigate the performance of homomorphic encryption using a real-world application, namely linear regression. Our main finding is that although the homomorphic computation of linear regression is in the range of minutes and thus slower than in the non-homomorphic case, linear regression can be computed homomorphic and is therefore suitable for use cases where data security is the top priority.}, address = {Singapore}, author = {Prantl, Thomas and Engel, Simon and Horn, Lukas and Kaiser, Dennis and Iffl\"ander, Lukas and Bauer, Andr\'e and Krupitzer, Christian and Kounev, Samuel}, booktitle = {Information Security Practice and Experience}, editor = {Meng, Weizhi and Yan, Zheng and Piuri, Vincenzo}, keywords = {descartes}, pages = {284–298}, publisher = {Springer Nature Singapore}, title = {Performance Impact Analysis of Homomorphic Encryption: A Case Study Using Linear Regression as an Example}, year = 2023 }

%0 Conference Paper %1 PrEnHoDeIfBaKrKo_ISPEC_De_Bello %A Prantl, Thomas %A Engel, Simon %A Horn, Lukas %A Kaiser, Dennis %A Iffländer, Lukas %A Bauer, André %A Krupitzer, Christian %A Kounev, Samuel %B Information Security Practice and Experience %C Singapore %D 2023 %E Meng, Weizhi %E Yan, Zheng %E Piuri, Vincenzo %I Springer Nature Singapore %P 284--298 %T Performance Impact Analysis of Homomorphic Encryption: A Case Study Using Linear Regression as an Example %X In recent years, the trend has increasingly been to store and process data in the cloud. However, this is based on the premise that cloud providers treat the data in a trustworthy manner. One way of using the data in the cloud without the provider having access to it is homomorphic encryption. However, since this encryption has only recently become practicable, analysis of its for practical applications is still in its infancy. Therefore, we investigate the performance of homomorphic encryption using a real-world application, namely linear regression. Our main finding is that although the homomorphic computation of linear regression is in the range of minutes and thus slower than in the non-homomorphic case, linear regression can be computed homomorphic and is therefore suitable for use cases where data security is the top priority. %@ 978-981-99-7032-2
{Towards Splitting Monolithic Workflows into Serverless Functions and Estimating Their Run-Time in the Earth Observation Domain}. D. Kaiser; B. Dovhan; A. Bauer; S. Kounev; in Proceedings of the 12th Symposium on Software Performance (SSP) (2021).

Extended Abstract
- [ BibTeX ]
- [ EndNote ]
@inproceedings{kaiser2021split, author = {Kaiser, Dennis and Dovhan, Bohdan and Bauer, Andr{\'e} and Kounev, Samuel}, booktitle = {Proceedings of the 12th Symposium on Software Performance (SSP)}, keywords = {severless}, month = 11, note = {Extended Abstract}, title = {{Towards Splitting Monolithic Workflows into Serverless Functions and Estimating Their Run-Time in the Earth Observation Domain}}, year = 2021 }

%0 Conference Paper %1 kaiser2021split %A Kaiser, Dennis %A Dovhan, Bohdan %A Bauer, Andr{é} %A Kounev, Samuel %B Proceedings of the 12th Symposium on Software Performance (SSP) %D 2021 %T {Towards Splitting Monolithic Workflows into Serverless Functions and Estimating Their Run-Time in the Earth Observation Domain}

Mastersthesis[ to top ]

{Towards Self-Aware Multirotor Formations}. D. Kaiser; Thesis; University of W{ü}rzburg; Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany. (2019, September).

W{ü}rzburg Software Engineering Award sponsored by infosim
- [ BibTeX ]
- [ EndNote ]
- [ Download ]
@mastersthesis{Kaiser2019, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Kaiser, Dennis}, keywords = {Self-aware-computing}, month = {09}, note = {<b>W{\"u}rzburg Software Engineering Award sponsored by infosim</b>}, school = {University of W{\"u}rzburg}, title = {{Towards Self-Aware Multirotor Formations}}, type = {{Master Thesis}}, year = 2019 }

%0 Thesis %1 Kaiser2019 %A Kaiser, Dennis %C Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany %D 2019 %T {Towards Self-Aware Multirotor Formations}

Hubland Süd, Geb. M2

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