What is the cost of privacy?

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F22%3AA2302FQL" target="_blank" >RIV/61988987:17310/22:A2302FQL - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-08974-9_55#citeas" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-08974-9_55#citeas</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-08974-9_55" target="_blank" >10.1007/978-3-031-08974-9_55</a>

Jazyk výsledku

angličtina

Název v původním jazyce

What is the cost of privacy?

Popis výsledku v původním jazyce

Grade research has to be replicable, thus the used data need to be publicly available. Speaking, e.g., about object detection task, where image data for autonomous driving also contain privacy information such as faces and license plates, the publication of data may be harmful to captured people. The solution to the moral dilemma is to anonymize the data. In this study, our aim is to investigate the effect of various anonymization techniques on the performance of algorithms that use such data. We discuss anonymization methods that remove and replace privacy data and select three methods to replace the privacy data: blurring, permutation, and replacing the area with a constant value. We adopted the Cityscapes dataset from which we extracted areas containing privacy information and are the manner of the anonymization methods. Our benchmark involves three famous object detectors: YOLOv3, Mask R-CNN with ResNet-50 backbone, and Mask R-CNN with Swin-T backbone. The results show that the impact of anonymization methods on the performance is negligible and the impact is similar for both convolutional-based and transformer-based backbones.

Název v anglickém jazyce

What is the cost of privacy?

Popis výsledku anglicky

Grade research has to be replicable, thus the used data need to be publicly available. Speaking, e.g., about object detection task, where image data for autonomous driving also contain privacy information such as faces and license plates, the publication of data may be harmful to captured people. The solution to the moral dilemma is to anonymize the data. In this study, our aim is to investigate the effect of various anonymization techniques on the performance of algorithms that use such data. We discuss anonymization methods that remove and replace privacy data and select three methods to replace the privacy data: blurring, permutation, and replacing the area with a constant value. We adopted the Cityscapes dataset from which we extracted areas containing privacy information and are the manner of the anonymization methods. Our benchmark involves three famous object detectors: YOLOv3, Mask R-CNN with ResNet-50 backbone, and Mask R-CNN with Swin-T backbone. The results show that the impact of anonymization methods on the performance is negligible and the impact is similar for both convolutional-based and transformer-based backbones.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Information Processing and Management of Uncertainty in Knowledge-Based Systems

ISBN

978-3-031-08974-9

ISSN

18650929

e-ISSN

—

Počet stran výsledku

11

Strana od-do

696-706

Název nakladatele

Springer International Publishing

Místo vydání

Cham

Místo konání akce

Milano

Datum konání akce

11. 7. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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