{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,19]],"date-time":"2025-04-19T09:30:17Z","timestamp":1745055017111,"version":"3.37.3"},"reference-count":48,"publisher":"Association for Computing Machinery (ACM)","issue":"2","funder":[{"name":"Federico Turrin, and Omitech S.r.l."}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Cyber-Phys. Syst."],"published-print":{"date-parts":[[2024,4,30]]},"abstract":"\n Electric Vehicles (EVs)<\/jats:bold>\n represent a green alternative to traditional fuel-powered vehicles. To enforce their widespread use, both the technical development and the security of users shall be guaranteed. Users\u2019 privacy represents a possible threat that impairs the adoption of EVs. In particular, recent works showed the feasibility of identifying EVs based on the current exchanged during the charging phase. In fact, while the resource negotiation phase runs over secure communication protocols, the signal exchanged during the actual charging contains features peculiar to each EV. In what is commonly known as profiling, a suitable feature extractor can associate such features to each EV.\n <\/jats:p>\n \n In this article, we propose\n EVScout2.0<\/jats:italic>\n , an extended and improved version of our previously proposed framework to profile EVs based on their charging behavior. By exploiting the current and pilot signals exchanged during the charging phase, our scheme can extract features peculiar for each EV, hence allowing their profiling. We implemented and tested\n EVScout2.0<\/jats:italic>\n over a set of real-world measurements considering over 7,500 charging sessions from a total of 137 EVs. In particular, numerical results show the superiority of\n EVScout2.0<\/jats:italic>\n with respect to the previous version.\n EVScout2.0<\/jats:italic>\n can profile EVs, attaining a maximum of 0.88 for both recall and precision scores in the case of a balanced dataset. To the best of the authors\u2019 knowledge, these results set a new benchmark for upcoming privacy research for large datasets of EVs.\n <\/jats:p>","DOI":"10.1145\/3565268","type":"journal-article","created":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T11:47:17Z","timestamp":1664365637000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["EVScout2.0<\/i>\n : Electric Vehicle Profiling through Charging Profile"],"prefix":"10.1145","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6138-2995","authenticated-orcid":false,"given":"Alessandro","family":"Brighente","sequence":"first","affiliation":[{"name":"Department of Mathematics, University of Padova, Padova, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3612-1934","authenticated-orcid":false,"given":"Mauro","family":"Conti","sequence":"additional","affiliation":[{"name":"Department of Mathematics, University of Padova, Padua, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7050-9369","authenticated-orcid":false,"given":"Denis","family":"Donadel","sequence":"additional","affiliation":[{"name":"Department of Mathematics, University of Padova, Padova, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5660-2447","authenticated-orcid":false,"given":"Federico","family":"Turrin","sequence":"additional","affiliation":[{"name":"Department of Mathematics, University of Padova, Padova, Italy"}]}],"member":"320","published-online":{"date-parts":[[2024,5,14]]},"reference":[{"issue":"4","key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"818","DOI":"10.3390\/wevj4040818","article-title":"Characteristics of CHAdeMO quick charging system","volume":"4","author":"Anegawa Takafumi","year":"2010","unstructured":"Takafumi Anegawa. 2010. 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