%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 %K descartes t_proceedings t_short myown %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.

@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.}, added-at = {2023-12-08T08:40:47.000+0100}, 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}, biburl = {https://www.bibsonomy.org/bibtex/2547e73c26557e8727fc633a1b6f41105/thomas.prantl}, booktitle = {Information Security Practice and Experience}, editor = {Meng, Weizhi and Yan, Zheng and Piuri, Vincenzo}, interhash = {0ea1b4a5e5c0cf8c0927851c2c7495cf}, intrahash = {547e73c26557e8727fc633a1b6f41105}, keywords = {descartes t_proceedings t_short myown}, pages = {284--298}, publisher = {Springer Nature Singapore}, timestamp = {2024-04-29T01:05:51.000+0200}, title = {Performance Impact Analysis of Homomorphic Encryption: A Case Study Using Linear Regression as an Example}, year = 2023 }