Preserving Privacy of Classified Authentic Satellite Lane Imagery Using Proxy Re-Encryption and UAV Technologies
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 area...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/7/1/53 |
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| author | Yarajarla Nagasree Chiramdasu Rupa Ponugumati Akshitha Gautam Srivastava Thippa Reddy Gadekallu Kuruva Lakshmanna |
| author_facet | Yarajarla Nagasree Chiramdasu Rupa Ponugumati Akshitha Gautam Srivastava Thippa Reddy Gadekallu Kuruva Lakshmanna |
| author_sort | Yarajarla Nagasree |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T13:00:05Z |
| format | Article |
| id | doaj.art-b3b7f17430be4c90b6769eae964b64aa |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-09T13:00:05Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-b3b7f17430be4c90b6769eae964b64aa2023-11-30T21:55:37ZengMDPI AGDrones2504-446X2023-01-01715310.3390/drones7010053Preserving Privacy of Classified Authentic Satellite Lane Imagery Using Proxy Re-Encryption and UAV TechnologiesYarajarla Nagasree0Chiramdasu Rupa1Ponugumati Akshitha2Gautam Srivastava3Thippa Reddy Gadekallu4Kuruva Lakshmanna5V. R Siddhartha Engineering College, Vijayawada 520007, IndiaV. R Siddhartha Engineering College, Vijayawada 520007, IndiaV. R Siddhartha Engineering College, Vijayawada 520007, IndiaDepartment of Mathematics and Computer Science, Brandon University, Brandon, MB R7A 6A9, CanadaSchool of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, IndiaSchool of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, IndiaPrivacy 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.https://www.mdpi.com/2504-446X/7/1/53Error Level Analysis (ELA)deep learningproxy re-encryptionfog computationUAV |
| spellingShingle | Yarajarla Nagasree Chiramdasu Rupa Ponugumati Akshitha Gautam Srivastava Thippa Reddy Gadekallu Kuruva Lakshmanna Preserving Privacy of Classified Authentic Satellite Lane Imagery Using Proxy Re-Encryption and UAV Technologies Drones Error Level Analysis (ELA) deep learning proxy re-encryption fog computation UAV |
| title | Preserving Privacy of Classified Authentic Satellite Lane Imagery Using Proxy Re-Encryption and UAV Technologies |
| title_full | Preserving Privacy of Classified Authentic Satellite Lane Imagery Using Proxy Re-Encryption and UAV Technologies |
| title_fullStr | Preserving Privacy of Classified Authentic Satellite Lane Imagery Using Proxy Re-Encryption and UAV Technologies |
| title_full_unstemmed | Preserving Privacy of Classified Authentic Satellite Lane Imagery Using Proxy Re-Encryption and UAV Technologies |
| title_short | Preserving Privacy of Classified Authentic Satellite Lane Imagery Using Proxy Re-Encryption and UAV Technologies |
| title_sort | preserving privacy of classified authentic satellite lane imagery using proxy re encryption and uav technologies |
| topic | Error Level Analysis (ELA) deep learning proxy re-encryption fog computation UAV |
| url | https://www.mdpi.com/2504-446X/7/1/53 |
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