Smart and Practical Privacy-Preserving Data Aggregation for Fog-Based Smart Grids
With the increasingly powerful and extensive deployment of edge devices, edge/fog computing enables customers to manage and analyze data locally, and extends computing power and data analysis applications to network edges. Meanwhile, as the next generation of the power grid, the smart grid can achie...
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Veröffentlicht in: | IEEE transactions on information forensics and security 2021, Vol.16, p.521-536 |
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creator | Zhao, Shuai Li, Fenghua Li, Hongwei Lu, Rongxing Ren, Siqi Bao, Haiyong Lin, Jian-Hong Han, Song |
description | With the increasingly powerful and extensive deployment of edge devices, edge/fog computing enables customers to manage and analyze data locally, and extends computing power and data analysis applications to network edges. Meanwhile, as the next generation of the power grid, the smart grid can achieve the goal of efficiency, economy, security, reliability, use safety and environmental friendliness for the power grid. However, privacy and secure issues in fog-based smart grid communications are challenging. Without proper protection, customers' privacy will be readily violated. This article presents a smart and practical Privacy-preserving Data Aggregation (PDA) scheme with smart pricing and packing method for fog-based smart grids, which achieves diversified tariffs, multifunctional statistics and efficiency. Especially, we first propose a smart PDA scheme with Smart Pricing (PDA-SP). With PDA-SP, the Control Center (CC) can compute more complex and higher-order aggregation statistics to provide various services, provide diversiform pricing strategies and choose a double-winning strategy. Subsequently, we put forward a practical PDA scheme with Packing Method (PDA-PM), which is able to reduce the size of encrypted data and improve performance in performing various secure computations. Moreover, we extend our original packing method and present a more useful packing method, which can handle general vectors with large entries. The security analysis shows that our proposed scheme is secure against many threats. The performance evaluation reveals that the computation and communication overheads of our proposed scheme are effectively reduced by employing the Somewhat Homomorphic Encryption (SHE), and our packing method can further significantly reduce these overheads. |
doi_str_mv | 10.1109/TIFS.2020.3014487 |
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Meanwhile, as the next generation of the power grid, the smart grid can achieve the goal of efficiency, economy, security, reliability, use safety and environmental friendliness for the power grid. However, privacy and secure issues in fog-based smart grid communications are challenging. Without proper protection, customers' privacy will be readily violated. This article presents a smart and practical Privacy-preserving Data Aggregation (PDA) scheme with smart pricing and packing method for fog-based smart grids, which achieves diversified tariffs, multifunctional statistics and efficiency. Especially, we first propose a smart PDA scheme with Smart Pricing (PDA-SP). With PDA-SP, the Control Center (CC) can compute more complex and higher-order aggregation statistics to provide various services, provide diversiform pricing strategies and choose a double-winning strategy. Subsequently, we put forward a practical PDA scheme with Packing Method (PDA-PM), which is able to reduce the size of encrypted data and improve performance in performing various secure computations. Moreover, we extend our original packing method and present a more useful packing method, which can handle general vectors with large entries. The security analysis shows that our proposed scheme is secure against many threats. The performance evaluation reveals that the computation and communication overheads of our proposed scheme are effectively reduced by employing the Somewhat Homomorphic Encryption (SHE), and our packing method can further significantly reduce these overheads.</description><identifier>ISSN: 1556-6013</identifier><identifier>EISSN: 1556-6021</identifier><identifier>DOI: 10.1109/TIFS.2020.3014487</identifier><identifier>CODEN: ITIFA6</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Additives ; Agglomeration ; Cloud computing ; Customers ; Data aggregation ; Data analysis ; Data management ; edge computing ; Encryption ; fog-based smart grid ; Handheld computers ; industrial Internet of Things ; packing method ; Performance enhancement ; Performance evaluation ; Power ; Pricing ; Privacy ; Security ; Smart grid ; Smart grids ; Smart meters ; Smart pricing ; somewhat homomorphic encryption ; Threat evaluation</subject><ispartof>IEEE transactions on information forensics and security, 2021, Vol.16, p.521-536</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c359t-f903a8a4f7f0bba9b2bb81b2ffeaf9bf907f7605bd0b6d87a8b48ee6467acce33</citedby><cites>FETCH-LOGICAL-c359t-f903a8a4f7f0bba9b2bb81b2ffeaf9bf907f7605bd0b6d87a8b48ee6467acce33</cites><orcidid>0000-0001-7758-3679 ; 0000-0002-1961-7946 ; 0000-0001-5720-0941</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9159620$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,4024,27923,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9159620$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Zhao, Shuai</creatorcontrib><creatorcontrib>Li, Fenghua</creatorcontrib><creatorcontrib>Li, Hongwei</creatorcontrib><creatorcontrib>Lu, Rongxing</creatorcontrib><creatorcontrib>Ren, Siqi</creatorcontrib><creatorcontrib>Bao, Haiyong</creatorcontrib><creatorcontrib>Lin, Jian-Hong</creatorcontrib><creatorcontrib>Han, Song</creatorcontrib><title>Smart and Practical Privacy-Preserving Data Aggregation for Fog-Based Smart Grids</title><title>IEEE transactions on information forensics and security</title><addtitle>TIFS</addtitle><description>With the increasingly powerful and extensive deployment of edge devices, edge/fog computing enables customers to manage and analyze data locally, and extends computing power and data analysis applications to network edges. Meanwhile, as the next generation of the power grid, the smart grid can achieve the goal of efficiency, economy, security, reliability, use safety and environmental friendliness for the power grid. However, privacy and secure issues in fog-based smart grid communications are challenging. Without proper protection, customers' privacy will be readily violated. This article presents a smart and practical Privacy-preserving Data Aggregation (PDA) scheme with smart pricing and packing method for fog-based smart grids, which achieves diversified tariffs, multifunctional statistics and efficiency. Especially, we first propose a smart PDA scheme with Smart Pricing (PDA-SP). With PDA-SP, the Control Center (CC) can compute more complex and higher-order aggregation statistics to provide various services, provide diversiform pricing strategies and choose a double-winning strategy. Subsequently, we put forward a practical PDA scheme with Packing Method (PDA-PM), which is able to reduce the size of encrypted data and improve performance in performing various secure computations. Moreover, we extend our original packing method and present a more useful packing method, which can handle general vectors with large entries. The security analysis shows that our proposed scheme is secure against many threats. The performance evaluation reveals that the computation and communication overheads of our proposed scheme are effectively reduced by employing the Somewhat Homomorphic Encryption (SHE), and our packing method can further significantly reduce these overheads.</description><subject>Additives</subject><subject>Agglomeration</subject><subject>Cloud computing</subject><subject>Customers</subject><subject>Data aggregation</subject><subject>Data analysis</subject><subject>Data management</subject><subject>edge computing</subject><subject>Encryption</subject><subject>fog-based smart grid</subject><subject>Handheld computers</subject><subject>industrial Internet of Things</subject><subject>packing method</subject><subject>Performance enhancement</subject><subject>Performance evaluation</subject><subject>Power</subject><subject>Pricing</subject><subject>Privacy</subject><subject>Security</subject><subject>Smart grid</subject><subject>Smart grids</subject><subject>Smart meters</subject><subject>Smart pricing</subject><subject>somewhat homomorphic encryption</subject><subject>Threat evaluation</subject><issn>1556-6013</issn><issn>1556-6021</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kFtrwkAQhZfSQq3tDyh9CfQ5dvaSzebRWrWCUIv2eZlNdkPEJnY3Cv77RiI-zYE5Zy4fIc8URpRC9rZZzNYjBgxGHKgQKr0hA5okMpbA6O1VU35PHkLYAghBpRqQ7_Uv-jbCuohWHvO2ynHXqeqI-SleeRusP1Z1GX1gi9G4LL0tsa2aOnKNj2ZNGb9jsEXUT5n7qgiP5M7hLtinSx2Sn9l0M_mMl1_zxWS8jHOeZG3sMuCoULjUgTGYGWaMooY5Z9FlpmunLpWQmAKMLFSKyghlrRQyxTy3nA_Jaz9375u_gw2t3jYHX3crNeueA1AJU52L9q7cNyF46_TeV92xJ01Bn8npMzl9Jqcv5LrMS5-prLVXf0aTTDLg_7tBano</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Zhao, Shuai</creator><creator>Li, Fenghua</creator><creator>Li, Hongwei</creator><creator>Lu, Rongxing</creator><creator>Ren, Siqi</creator><creator>Bao, Haiyong</creator><creator>Lin, Jian-Hong</creator><creator>Han, Song</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Subsequently, we put forward a practical PDA scheme with Packing Method (PDA-PM), which is able to reduce the size of encrypted data and improve performance in performing various secure computations. Moreover, we extend our original packing method and present a more useful packing method, which can handle general vectors with large entries. The security analysis shows that our proposed scheme is secure against many threats. The performance evaluation reveals that the computation and communication overheads of our proposed scheme are effectively reduced by employing the Somewhat Homomorphic Encryption (SHE), and our packing method can further significantly reduce these overheads.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TIFS.2020.3014487</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0001-7758-3679</orcidid><orcidid>https://orcid.org/0000-0002-1961-7946</orcidid><orcidid>https://orcid.org/0000-0001-5720-0941</orcidid></addata></record> |
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subjects | Additives Agglomeration Cloud computing Customers Data aggregation Data analysis Data management edge computing Encryption fog-based smart grid Handheld computers industrial Internet of Things packing method Performance enhancement Performance evaluation Power Pricing Privacy Security Smart grid Smart grids Smart meters Smart pricing somewhat homomorphic encryption Threat evaluation |
title | Smart and Practical Privacy-Preserving Data Aggregation for Fog-Based Smart Grids |
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