Privacy-Preserving Fog Aggregation of Smart Grid Data Using Dynamic Differentially-Private Data Perturbation
<p>The edge of the smart grid has a massive number of power and resource-constrained interconnected devices. Mainly, smart meters report power consumption data from consumer homes, industrial buildings, and other connected infrastructures. Multiple approaches were proposed in the literature to...
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| مؤلفون آخرون: | , |
| منشور في: |
2022
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إضافة وسم
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| _version_ | 1864513561101860864 |
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| author | Fawaz Kserawi (16904859) |
| author2 | Saeed Al-Marri (16904862) Qutaibah Malluhi (3158757) |
| author2_role | author author |
| author_facet | Fawaz Kserawi (16904859) Saeed Al-Marri (16904862) Qutaibah Malluhi (3158757) |
| author_role | author |
| dc.creator.none.fl_str_mv | Fawaz Kserawi (16904859) Saeed Al-Marri (16904862) Qutaibah Malluhi (3158757) |
| dc.date.none.fl_str_mv | 2022-04-28T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2022.3167015 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Privacy-Preserving_Fog_Aggregation_of_Smart_Grid_Data_Using_Dynamic_Differentially-Private_Data_Perturbation/24056436 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Information and computing sciences Distributed computing and systems software Differential privacy Smart meters Data aggregation Batteries Smart grids Privacy Servers Advanced metering infrastructure Electrical grid The Internet of Things Information privacy Smart grid Smart meter |
| dc.title.none.fl_str_mv | Privacy-Preserving Fog Aggregation of Smart Grid Data Using Dynamic Differentially-Private Data Perturbation |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>The edge of the smart grid has a massive number of power and resource-constrained interconnected devices. Mainly, smart meters report power consumption data from consumer homes, industrial buildings, and other connected infrastructures. Multiple approaches were proposed in the literature to preserve the privacy of consumers by altering the data via additive noise, masking, or other data obfuscation techniques. A significant body of work in the literature employs differential privacy methods with constraining predefined parameters to achieve the optimal trade-off between privacy and utility of the data. However, billing accuracy can be degraded by using such additive noise techniques. We propose a differentially-private model that perturbs data by adding noise obtained from a virtual chargeable battery, while maintaining billing accuracy. Our model utilizes fog-computing data aggregation with lightweight cryptographic primitives to ensure the authenticity and confidentiality of data generated by low-end devices. We describe our differentially-private model with flexible constraints and a dynamic window algorithm to maintain the privacy-budget loss in infinitely generated time-series data. Our experimental results show a possible decrease in data perturbation error by 51.7% and 61.2% for smart meters and fog-computing data aggregators perturbed data, respectively, compared to the commonly used Gaussian mechanism.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2022.3167015" target="_blank">https://dx.doi.org/10.1109/access.2022.3167015</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_98ffad7c4021d35075c58081d037eafe |
| identifier_str_mv | 10.1109/access.2022.3167015 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24056436 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Privacy-Preserving Fog Aggregation of Smart Grid Data Using Dynamic Differentially-Private Data PerturbationFawaz Kserawi (16904859)Saeed Al-Marri (16904862)Qutaibah Malluhi (3158757)EngineeringElectrical engineeringInformation and computing sciencesDistributed computing and systems softwareDifferential privacySmart metersData aggregationBatteriesSmart gridsPrivacyServersAdvanced metering infrastructureElectrical gridThe Internet of ThingsInformation privacySmart gridSmart meter<p>The edge of the smart grid has a massive number of power and resource-constrained interconnected devices. Mainly, smart meters report power consumption data from consumer homes, industrial buildings, and other connected infrastructures. Multiple approaches were proposed in the literature to preserve the privacy of consumers by altering the data via additive noise, masking, or other data obfuscation techniques. A significant body of work in the literature employs differential privacy methods with constraining predefined parameters to achieve the optimal trade-off between privacy and utility of the data. However, billing accuracy can be degraded by using such additive noise techniques. We propose a differentially-private model that perturbs data by adding noise obtained from a virtual chargeable battery, while maintaining billing accuracy. Our model utilizes fog-computing data aggregation with lightweight cryptographic primitives to ensure the authenticity and confidentiality of data generated by low-end devices. We describe our differentially-private model with flexible constraints and a dynamic window algorithm to maintain the privacy-budget loss in infinitely generated time-series data. Our experimental results show a possible decrease in data perturbation error by 51.7% and 61.2% for smart meters and fog-computing data aggregators perturbed data, respectively, compared to the commonly used Gaussian mechanism.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2022.3167015" target="_blank">https://dx.doi.org/10.1109/access.2022.3167015</a></p>2022-04-28T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2022.3167015https://figshare.com/articles/journal_contribution/Privacy-Preserving_Fog_Aggregation_of_Smart_Grid_Data_Using_Dynamic_Differentially-Private_Data_Perturbation/24056436CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240564362022-04-28T00:00:00Z |
| spellingShingle | Privacy-Preserving Fog Aggregation of Smart Grid Data Using Dynamic Differentially-Private Data Perturbation Fawaz Kserawi (16904859) Engineering Electrical engineering Information and computing sciences Distributed computing and systems software Differential privacy Smart meters Data aggregation Batteries Smart grids Privacy Servers Advanced metering infrastructure Electrical grid The Internet of Things Information privacy Smart grid Smart meter |
| status_str | publishedVersion |
| title | Privacy-Preserving Fog Aggregation of Smart Grid Data Using Dynamic Differentially-Private Data Perturbation |
| title_full | Privacy-Preserving Fog Aggregation of Smart Grid Data Using Dynamic Differentially-Private Data Perturbation |
| title_fullStr | Privacy-Preserving Fog Aggregation of Smart Grid Data Using Dynamic Differentially-Private Data Perturbation |
| title_full_unstemmed | Privacy-Preserving Fog Aggregation of Smart Grid Data Using Dynamic Differentially-Private Data Perturbation |
| title_short | Privacy-Preserving Fog Aggregation of Smart Grid Data Using Dynamic Differentially-Private Data Perturbation |
| title_sort | Privacy-Preserving Fog Aggregation of Smart Grid Data Using Dynamic Differentially-Private Data Perturbation |
| topic | Engineering Electrical engineering Information and computing sciences Distributed computing and systems software Differential privacy Smart meters Data aggregation Batteries Smart grids Privacy Servers Advanced metering infrastructure Electrical grid The Internet of Things Information privacy Smart grid Smart meter |