Secured cloud-based image data processing of self-driving vehicles using full homomorphic encryption
Self-driving vehicles leverage internet of things (IoT) technology to utilize multiple sensors to continuously monitor their environment and make decisions without human intervention. Data collected from these sensors require secure transmission and encrypted storage in cloud servers, where retrievi...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-10-01
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| Series: | Kuwait Journal of Science |
| Subjects: | |
| Online Access: | https://www.sciencedirect.com/science/article/pii/S2307410825000938 |
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| _version_ | 1839648261925765120 |
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| author | Kamran Saeed M.Fatih Adak |
| author_facet | Kamran Saeed M.Fatih Adak |
| author_sort | Kamran Saeed |
| collection | DOAJ |
| description | Self-driving vehicles leverage internet of things (IoT) technology to utilize multiple sensors to continuously monitor their environment and make decisions without human intervention. Data collected from these sensors require secure transmission and encrypted storage in cloud servers, where retrieving the data without decryption is necessary to ensure privacy. A novel method using full homomorphic encryption (FHE) image-based data is proposed, incorporating three different methods to search for the desired encrypted images stored in the cloud. This method involves converting images and objects detected by YOLOv9c into pixel data, then applying the Brakerski/Fan-Vercauteren (BFV) scheme to encrypt the data and store it on the cloud. Administrators can search through the encrypted images using the combination of techniques, including percentile similarity-based image detection, unknown object-based image detection, and known object-based image detection from encrypted images. FHE is used to provide various secure search approaches, contrasting with conventional index-based encrypted searching. The proposed solution provides a mechanism in the scenario of self-driving vehicles where object/image detection stored in the cloud can be done without decrypting it, hence enhancing privacy and security of data on the cloud generated by self-driving vehicles and IoT devices. |
| format | Article |
| id | doaj-art-d2788da086ea48a6920b49e1c7f6e69c |
| institution | Matheson Library |
| issn | 2307-4108 2307-4116 |
| language | English |
| publishDate | 2025-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Kuwait Journal of Science |
| spelling | doaj-art-d2788da086ea48a6920b49e1c7f6e69c2025-06-29T07:09:18ZengElsevierKuwait Journal of Science2307-41082307-41162025-10-01524100449https://doi.org/10.1016/j.kjs.2025.100449Secured cloud-based image data processing of self-driving vehicles using full homomorphic encryptionKamran Saeed0https://orcid.org/0000-0002-6172-2926M.Fatih Adak1Sakarya University, Computer Engineering Department, Sakarya, TurkeySakarya University, Computer Engineering Department, Sakarya, TurkeySelf-driving vehicles leverage internet of things (IoT) technology to utilize multiple sensors to continuously monitor their environment and make decisions without human intervention. Data collected from these sensors require secure transmission and encrypted storage in cloud servers, where retrieving the data without decryption is necessary to ensure privacy. A novel method using full homomorphic encryption (FHE) image-based data is proposed, incorporating three different methods to search for the desired encrypted images stored in the cloud. This method involves converting images and objects detected by YOLOv9c into pixel data, then applying the Brakerski/Fan-Vercauteren (BFV) scheme to encrypt the data and store it on the cloud. Administrators can search through the encrypted images using the combination of techniques, including percentile similarity-based image detection, unknown object-based image detection, and known object-based image detection from encrypted images. FHE is used to provide various secure search approaches, contrasting with conventional index-based encrypted searching. The proposed solution provides a mechanism in the scenario of self-driving vehicles where object/image detection stored in the cloud can be done without decrypting it, hence enhancing privacy and security of data on the cloud generated by self-driving vehicles and IoT devices.https://www.sciencedirect.com/science/article/pii/S2307410825000938homomorphic encryptionself-driving vehiclesbfv schemecloud serverencrypted searching algorithmencrypted images technique |
| spellingShingle | Kamran Saeed M.Fatih Adak Secured cloud-based image data processing of self-driving vehicles using full homomorphic encryption Kuwait Journal of Science homomorphic encryption self-driving vehicles bfv scheme cloud server encrypted searching algorithm encrypted images technique |
| title | Secured cloud-based image data processing of self-driving vehicles using full homomorphic encryption |
| title_full | Secured cloud-based image data processing of self-driving vehicles using full homomorphic encryption |
| title_fullStr | Secured cloud-based image data processing of self-driving vehicles using full homomorphic encryption |
| title_full_unstemmed | Secured cloud-based image data processing of self-driving vehicles using full homomorphic encryption |
| title_short | Secured cloud-based image data processing of self-driving vehicles using full homomorphic encryption |
| title_sort | secured cloud based image data processing of self driving vehicles using full homomorphic encryption |
| topic | homomorphic encryption self-driving vehicles bfv scheme cloud server encrypted searching algorithm encrypted images technique |
| url | https://www.sciencedirect.com/science/article/pii/S2307410825000938 |
| work_keys_str_mv | AT kamransaeed securedcloudbasedimagedataprocessingofselfdrivingvehiclesusingfullhomomorphicencryption AT mfatihadak securedcloudbasedimagedataprocessingofselfdrivingvehiclesusingfullhomomorphicencryption |