Preserving Privacy of Classified Authentic Satellite Lane Imagery Using Proxy Re-Encryption and UAV Technologies

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Preserving Privacy of Classified Authentic Satellite Lane Imagery Using Proxy Re-Encryption and UAV Technologies

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Yarajarla Nagasree / Chiramdasu Rupa / Ponugumati Akshitha / Gautam Srivastava / Thippa Reddy Gadekallu / Kuruva Lakshmanna

Abstract

Privacy preservation of image data has been a top priority for many applications. The rapid growth of technology has increased the possibility of creating fake images using social media as a platform. However, many people, including researchers, rely on image data for various purposes. In rural areas, lane images have a high level of importance, as this data can be used for analyzing various lane conditions. However, this data is also being forged. To overcome this and to improve the privacy of lane image data, a real-time solution is proposed in this work. The proposed methodology assumes lane images as input, which are further classified as fake or bona fide images with the help of Error Level Analysis (ELA) and artificial neural network (ANN) algorithms. The U-Net model ensures lane detection for bona fide lane images, which helps in the easy identification of lanes in rural areas. The final images obtained are secured by using the proxy re-encryption technique which uses RSA and ECC algorithms. This helps in ensuring the privacy of lane images. The cipher images are maintained using fog computing and processed with integrity. The proposed methodology is necessary for protecting genuine satellite lane images in rural areas, which are further used by forecasters, and researchers for making interpretations and predictions on data.

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Dokumentinformationen

Titel :

Preserving Privacy of Classified Authentic Satellite Lane Imagery Using Proxy Re-Encryption and UAV Technologies

Beteiligte:

Yarajarla Nagasree (Autor:in) / Chiramdasu Rupa (Autor:in) / Ponugumati Akshitha (Autor:in) / Gautam Srivastava (Autor:in) / Thippa Reddy Gadekallu (Autor:in) / Kuruva Lakshmanna (Autor:in)

Erschienen in:

Drones, Vol 7, Iss 1, p 53 (2023)

Erscheinungsdatum :

2023

ISSN :

2504-446X

DOI :

https://doi.org/10.3390/drones7010053

Medientyp :

Aufsatz (Zeitschrift)

Format :

Elektronische Ressource