Secure and Robust Deep Learning Using Homomorphic Encryption and Adversarial Approach for Image Classification
Thesis (M.S.) -- Computer Science, January 2024.
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| Format: | masterThesis |
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2024
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| Online Access: | http://hdl.handle.net/10725/15956 https://doi.org/10.26756/th.2023.685 http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php |
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| _version_ | 1864513471809323008 |
|---|---|
| author | Abbass, Ali |
| author_facet | Abbass, Ali |
| author_role | author |
| dc.creator.none.fl_str_mv | Abbass, Ali |
| dc.date.none.fl_str_mv | 2024-08-05T07:48:07Z 2024-08-05T07:48:07Z 2024 2024-01-15 |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10725/15956 https://doi.org/10.26756/th.2023.685 http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | Lebanese American University |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Deep learning (Machine learning) -- Case studies Computer security -- Case studies Data encryption (Computer science) Lebanese American University -- Dissertations Dissertations, Academic |
| dc.title.none.fl_str_mv | Secure and Robust Deep Learning Using Homomorphic Encryption and Adversarial Approach for Image Classification |
| dc.type.none.fl_str_mv | Thesis info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/masterThesis |
| description | Thesis (M.S.) -- Computer Science, January 2024. |
| eu_rights_str_mv | openAccess |
| format | masterThesis |
| id | LAURepo_b107497f45a268dccbcde88882f72d0f |
| language_invalid_str_mv | en |
| network_acronym_str | LAURepo |
| network_name_str | Lebanese American University repository |
| oai_identifier_str | oai:laur.lau.edu.lb:10725/15956 |
| publishDate | 2024 |
| publisher.none.fl_str_mv | Lebanese American University |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Secure and Robust Deep Learning Using Homomorphic Encryption and Adversarial Approach for Image ClassificationAbbass, AliDeep learning (Machine learning) -- Case studiesComputer security -- Case studiesData encryption (Computer science)Lebanese American University -- DissertationsDissertations, AcademicThesis (M.S.) -- Computer Science, January 2024.The field of deep learning is facing some complex challenges when it comes to balancing sensitive data with privacy and security. With the emergence of quantum computers, encryption vulnerabilities have become a major concern. However, there is a promising solution in the form of fully homomorphic encryption (FHE) that enables encryption without decryption, creating a secure environment. To further enhance the security of deep learning models, we can employ techniques like conditional GANs. We are excited to present a novel PPDL approach for image classification that integrates FHE with adversarial learning to improve resilience. However, it is essential to note such an approach comes with a high computational cost and longer runtime. Nonetheless, it is a small price to pay for the extra layer of security it provides. . Our Research combined fully homomorphic encryption and adversarial machine learning to develop a reliable and accurate model. We protected sensitive information with CKKS encryption. The custom dataset, created with Conditional GANS, showed a 94% accuracy rate when tested with a CNN model. However, when we encrypted the model and dataset using CKKS, the accuracy dropped slightly to 92%. Our findings hold promise for future research and we are excited to share them with you.1 online resource (xi, 60 leaves): ill. (some col.)Includes bibliographical references (leaves 52-60)Lebanese American University2024-08-05T07:48:07Z2024-08-05T07:48:07Z20242024-01-15Thesisinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesishttp://hdl.handle.net/10725/15956https://doi.org/10.26756/th.2023.685http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.phpeninfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/159562024-12-06T06:24:41Z |
| spellingShingle | Secure and Robust Deep Learning Using Homomorphic Encryption and Adversarial Approach for Image Classification Abbass, Ali Deep learning (Machine learning) -- Case studies Computer security -- Case studies Data encryption (Computer science) Lebanese American University -- Dissertations Dissertations, Academic |
| status_str | publishedVersion |
| title | Secure and Robust Deep Learning Using Homomorphic Encryption and Adversarial Approach for Image Classification |
| title_full | Secure and Robust Deep Learning Using Homomorphic Encryption and Adversarial Approach for Image Classification |
| title_fullStr | Secure and Robust Deep Learning Using Homomorphic Encryption and Adversarial Approach for Image Classification |
| title_full_unstemmed | Secure and Robust Deep Learning Using Homomorphic Encryption and Adversarial Approach for Image Classification |
| title_short | Secure and Robust Deep Learning Using Homomorphic Encryption and Adversarial Approach for Image Classification |
| title_sort | Secure and Robust Deep Learning Using Homomorphic Encryption and Adversarial Approach for Image Classification |
| topic | Deep learning (Machine learning) -- Case studies Computer security -- Case studies Data encryption (Computer science) Lebanese American University -- Dissertations Dissertations, Academic |
| url | http://hdl.handle.net/10725/15956 https://doi.org/10.26756/th.2023.685 http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php |