Privacy Preservation of Data-Driven Models in Smart Grids Using Homomorphic Encryption
<p dir="ltr">Deep learning models have been applied for varied electrical applications in smart grids with a high degree of reliability and accuracy. The development of deep learning models requires the historical data collected from several electric utilities during the training of...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , |
| منشور في: |
2020
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513515827494912 |
|---|---|
| author | Dabeeruddin Syed (16864260) |
| author2 | Shady S. Refaat (16864269) Othmane Bouhali (8252544) |
| author2_role | author author |
| author_facet | Dabeeruddin Syed (16864260) Shady S. Refaat (16864269) Othmane Bouhali (8252544) |
| author_role | author |
| dc.creator.none.fl_str_mv | Dabeeruddin Syed (16864260) Shady S. Refaat (16864269) Othmane Bouhali (8252544) |
| dc.date.none.fl_str_mv | 2020-07-08T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/info11070357 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Privacy_Preservation_of_Data-Driven_Models_in_Smart_Grids_Using_Homomorphic_Encryption/25835260 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Cybersecurity and privacy Machine learning Deep learnings homomorphic encryption fault localization smart grids deep neural networks |
| dc.title.none.fl_str_mv | Privacy Preservation of Data-Driven Models in Smart Grids Using Homomorphic Encryption |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Deep learning models have been applied for varied electrical applications in smart grids with a high degree of reliability and accuracy. The development of deep learning models requires the historical data collected from several electric utilities during the training of the models. The lack of historical data for training and testing of developed models, considering security and privacy policy restrictions, is considered one of the greatest challenges to machine learning-based techniques. The paper proposes the use of homomorphic encryption, which enables the possibility of training the deep learning and classical machine learning models whilst preserving the privacy and security of the data. The proposed methodology is tested for applications of fault identification and localization, and load forecasting in smart grids. The results for fault localization show that the classification accuracy of the proposed privacy-preserving deep learning model while using homomorphic encryption is 97–98%, which is close to 98–99% classification accuracy of the model on plain data. Additionally, for load forecasting application, the results show that RMSE using the homomorphic encryption model is 0.0352 MWh while RMSE without application of encryption in modeling is around 0.0248 MWh.</p><h2>Other Information</h2><p dir="ltr">Published in: Information<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/info11070357" target="_blank">https://dx.doi.org/10.3390/info11070357</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_85303e713fb0a582e274a6bbb1924a5b |
| identifier_str_mv | 10.3390/info11070357 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25835260 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Privacy Preservation of Data-Driven Models in Smart Grids Using Homomorphic EncryptionDabeeruddin Syed (16864260)Shady S. Refaat (16864269)Othmane Bouhali (8252544)Information and computing sciencesArtificial intelligenceCybersecurity and privacyMachine learningDeep learningshomomorphic encryptionfault localizationsmart gridsdeep neural networks<p dir="ltr">Deep learning models have been applied for varied electrical applications in smart grids with a high degree of reliability and accuracy. The development of deep learning models requires the historical data collected from several electric utilities during the training of the models. The lack of historical data for training and testing of developed models, considering security and privacy policy restrictions, is considered one of the greatest challenges to machine learning-based techniques. The paper proposes the use of homomorphic encryption, which enables the possibility of training the deep learning and classical machine learning models whilst preserving the privacy and security of the data. The proposed methodology is tested for applications of fault identification and localization, and load forecasting in smart grids. The results for fault localization show that the classification accuracy of the proposed privacy-preserving deep learning model while using homomorphic encryption is 97–98%, which is close to 98–99% classification accuracy of the model on plain data. Additionally, for load forecasting application, the results show that RMSE using the homomorphic encryption model is 0.0352 MWh while RMSE without application of encryption in modeling is around 0.0248 MWh.</p><h2>Other Information</h2><p dir="ltr">Published in: Information<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/info11070357" target="_blank">https://dx.doi.org/10.3390/info11070357</a></p>2020-07-08T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/info11070357https://figshare.com/articles/journal_contribution/Privacy_Preservation_of_Data-Driven_Models_in_Smart_Grids_Using_Homomorphic_Encryption/25835260CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/258352602020-07-08T03:00:00Z |
| spellingShingle | Privacy Preservation of Data-Driven Models in Smart Grids Using Homomorphic Encryption Dabeeruddin Syed (16864260) Information and computing sciences Artificial intelligence Cybersecurity and privacy Machine learning Deep learnings homomorphic encryption fault localization smart grids deep neural networks |
| status_str | publishedVersion |
| title | Privacy Preservation of Data-Driven Models in Smart Grids Using Homomorphic Encryption |
| title_full | Privacy Preservation of Data-Driven Models in Smart Grids Using Homomorphic Encryption |
| title_fullStr | Privacy Preservation of Data-Driven Models in Smart Grids Using Homomorphic Encryption |
| title_full_unstemmed | Privacy Preservation of Data-Driven Models in Smart Grids Using Homomorphic Encryption |
| title_short | Privacy Preservation of Data-Driven Models in Smart Grids Using Homomorphic Encryption |
| title_sort | Privacy Preservation of Data-Driven Models in Smart Grids Using Homomorphic Encryption |
| topic | Information and computing sciences Artificial intelligence Cybersecurity and privacy Machine learning Deep learnings homomorphic encryption fault localization smart grids deep neural networks |