Extracting vehicle sensor signals from CAN logs for driver re-identification Article Swipe
Szilvia Lestyán
,
Gergely Ács
,
Gergely Biczók
,
Zsolt Szalay
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1902.08956
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1902.08956
Data is the new oil for the car industry. Cars generate data about how they are used and who's behind the wheel which gives rise to a novel way of profiling individuals. Several prior works have successfully demonstrated the feasibility of driver re-identification using the in-vehicle network data captured on the vehicle's CAN (Controller Area Network) bus. However, all of them used signals (e.g., velocity, brake pedal or accelerator position) that have already been extracted from the CAN log which is itself not a straightforward process. Indeed, car manufacturers intentionally do not reveal the exact signal location within CAN logs. Nevertheless, we show that signals can be efficiently extracted from CAN logs using machine learning techniques. We exploit that signals have several distinguishing statistical features which can be learnt and effectively used to identify them across different vehicles, that is, to quasi "reverse-engineer" the CAN protocol. We also demonstrate that the extracted signals can be successfully used to re-identify individuals in a dataset of 33 drivers. Therefore, not revealing signal locations in CAN logs per se does not prevent them to be regarded as personal data of drivers.
Related Topics
Concepts
Computer science
Profiling (computer programming)
Identification (biology)
Brake
Exploit
Process (computing)
Automotive industry
SIGNAL (programming language)
CAN bus
Real-time computing
Data mining
Artificial intelligence
Engineering
Automotive engineering
Computer hardware
Computer security
Biology
Botany
Operating system
Programming language
Aerospace engineering
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1902.08956
- https://arxiv.org/pdf/1902.08956
- OA Status
- green
- Cited By
- 2
- References
- 10
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2952186362
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2952186362Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.1902.08956Digital Object Identifier
- Title
-
Extracting vehicle sensor signals from CAN logs for driver re-identificationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-02-24Full publication date if available
- Authors
-
Szilvia Lestyán, Gergely Ács, Gergely Biczók, Zsolt SzalayList of authors in order
- Landing page
-
https://arxiv.org/abs/1902.08956Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1902.08956Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1902.08956Direct OA link when available
- Concepts
-
Computer science, Profiling (computer programming), Identification (biology), Brake, Exploit, Process (computing), Automotive industry, SIGNAL (programming language), CAN bus, Real-time computing, Data mining, Artificial intelligence, Engineering, Automotive engineering, Computer hardware, Computer security, Biology, Botany, Operating system, Programming language, Aerospace engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2020: 2Per-year citation counts (last 5 years)
- References (count)
-
10Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2952186362 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.1902.08956 |
| ids.doi | https://doi.org/10.48550/arxiv.1902.08956 |
| ids.mag | 2952186362 |
| ids.openalex | https://openalex.org/W2952186362 |
| fwci | |
| type | preprint |
| title | Extracting vehicle sensor signals from CAN logs for driver re-identification |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11099 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9993000030517578 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2203 |
| topics[0].subfield.display_name | Automotive Engineering |
| topics[0].display_name | Autonomous Vehicle Technology and Safety |
| topics[1].id | https://openalex.org/T10761 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9977999925613403 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2208 |
| topics[1].subfield.display_name | Electrical and Electronic Engineering |
| topics[1].display_name | Vehicular Ad Hoc Networks (VANETs) |
| topics[2].id | https://openalex.org/T11344 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9961000084877014 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2215 |
| topics[2].subfield.display_name | Building and Construction |
| topics[2].display_name | Traffic Prediction and Management Techniques |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.6640833020210266 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C187191949 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6199877262115479 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1138496 |
| concepts[1].display_name | Profiling (computer programming) |
| concepts[2].id | https://openalex.org/C116834253 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6015679240226746 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q2039217 |
| concepts[2].display_name | Identification (biology) |
| concepts[3].id | https://openalex.org/C2780999251 |
| concepts[3].level | 2 |
| concepts[3].score | 0.589530348777771 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q17022503 |
| concepts[3].display_name | Brake |
| concepts[4].id | https://openalex.org/C165696696 |
| concepts[4].level | 2 |
| concepts[4].score | 0.582648754119873 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11287 |
| concepts[4].display_name | Exploit |
| concepts[5].id | https://openalex.org/C98045186 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5201233625411987 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q205663 |
| concepts[5].display_name | Process (computing) |
| concepts[6].id | https://openalex.org/C526921623 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4816502630710602 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q190117 |
| concepts[6].display_name | Automotive industry |
| concepts[7].id | https://openalex.org/C2779843651 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4561845362186432 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q7390335 |
| concepts[7].display_name | SIGNAL (programming language) |
| concepts[8].id | https://openalex.org/C201762086 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4507911801338196 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q728183 |
| concepts[8].display_name | CAN bus |
| concepts[9].id | https://openalex.org/C79403827 |
| concepts[9].level | 1 |
| concepts[9].score | 0.42417672276496887 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q3988 |
| concepts[9].display_name | Real-time computing |
| concepts[10].id | https://openalex.org/C124101348 |
| concepts[10].level | 1 |
| concepts[10].score | 0.4231567978858948 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[10].display_name | Data mining |
| concepts[11].id | https://openalex.org/C154945302 |
| concepts[11].level | 1 |
| concepts[11].score | 0.33326268196105957 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[11].display_name | Artificial intelligence |
| concepts[12].id | https://openalex.org/C127413603 |
| concepts[12].level | 0 |
| concepts[12].score | 0.24978649616241455 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[12].display_name | Engineering |
| concepts[13].id | https://openalex.org/C171146098 |
| concepts[13].level | 1 |
| concepts[13].score | 0.2028006613254547 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q124192 |
| concepts[13].display_name | Automotive engineering |
| concepts[14].id | https://openalex.org/C9390403 |
| concepts[14].level | 1 |
| concepts[14].score | 0.1781211495399475 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q3966 |
| concepts[14].display_name | Computer hardware |
| concepts[15].id | https://openalex.org/C38652104 |
| concepts[15].level | 1 |
| concepts[15].score | 0.13186043500900269 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q3510521 |
| concepts[15].display_name | Computer security |
| concepts[16].id | https://openalex.org/C86803240 |
| concepts[16].level | 0 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[16].display_name | Biology |
| concepts[17].id | https://openalex.org/C59822182 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q441 |
| concepts[17].display_name | Botany |
| concepts[18].id | https://openalex.org/C111919701 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[18].display_name | Operating system |
| concepts[19].id | https://openalex.org/C199360897 |
| concepts[19].level | 1 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[19].display_name | Programming language |
| concepts[20].id | https://openalex.org/C146978453 |
| concepts[20].level | 1 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q3798668 |
| concepts[20].display_name | Aerospace engineering |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.6640833020210266 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/profiling |
| keywords[1].score | 0.6199877262115479 |
| keywords[1].display_name | Profiling (computer programming) |
| keywords[2].id | https://openalex.org/keywords/identification |
| keywords[2].score | 0.6015679240226746 |
| keywords[2].display_name | Identification (biology) |
| keywords[3].id | https://openalex.org/keywords/brake |
| keywords[3].score | 0.589530348777771 |
| keywords[3].display_name | Brake |
| keywords[4].id | https://openalex.org/keywords/exploit |
| keywords[4].score | 0.582648754119873 |
| keywords[4].display_name | Exploit |
| keywords[5].id | https://openalex.org/keywords/process |
| keywords[5].score | 0.5201233625411987 |
| keywords[5].display_name | Process (computing) |
| keywords[6].id | https://openalex.org/keywords/automotive-industry |
| keywords[6].score | 0.4816502630710602 |
| keywords[6].display_name | Automotive industry |
| keywords[7].id | https://openalex.org/keywords/signal |
| keywords[7].score | 0.4561845362186432 |
| keywords[7].display_name | SIGNAL (programming language) |
| keywords[8].id | https://openalex.org/keywords/can-bus |
| keywords[8].score | 0.4507911801338196 |
| keywords[8].display_name | CAN bus |
| keywords[9].id | https://openalex.org/keywords/real-time-computing |
| keywords[9].score | 0.42417672276496887 |
| keywords[9].display_name | Real-time computing |
| keywords[10].id | https://openalex.org/keywords/data-mining |
| keywords[10].score | 0.4231567978858948 |
| keywords[10].display_name | Data mining |
| keywords[11].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[11].score | 0.33326268196105957 |
| keywords[11].display_name | Artificial intelligence |
| keywords[12].id | https://openalex.org/keywords/engineering |
| keywords[12].score | 0.24978649616241455 |
| keywords[12].display_name | Engineering |
| keywords[13].id | https://openalex.org/keywords/automotive-engineering |
| keywords[13].score | 0.2028006613254547 |
| keywords[13].display_name | Automotive engineering |
| keywords[14].id | https://openalex.org/keywords/computer-hardware |
| keywords[14].score | 0.1781211495399475 |
| keywords[14].display_name | Computer hardware |
| keywords[15].id | https://openalex.org/keywords/computer-security |
| keywords[15].score | 0.13186043500900269 |
| keywords[15].display_name | Computer security |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:1902.08956 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/1902.08956 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/1902.08956 |
| locations[1].id | mag:2952186362 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | arXiv (Cornell University) |
| locations[1].landing_page_url | https://arxiv.org/pdf/1902.08956.pdf |
| locations[2].id | doi:10.48550/arxiv.1902.08956 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400194 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | True |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | arXiv (Cornell University) |
| locations[2].source.host_organization | https://openalex.org/I205783295 |
| locations[2].source.host_organization_name | Cornell University |
| locations[2].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | |
| locations[2].raw_type | article |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | |
| locations[2].raw_source_name | |
| locations[2].landing_page_url | https://doi.org/10.48550/arxiv.1902.08956 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5013637193 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Szilvia Lestyán |
| authorships[0].countries | HU |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I29770179 |
| authorships[0].affiliations[0].raw_affiliation_string | CrySyS Lab, Dept. of Networked Systems and Services, Budapest Univ. of Technology and Economics and Hungary, --- Select a Country --- |
| authorships[0].institutions[0].id | https://openalex.org/I29770179 |
| authorships[0].institutions[0].ror | https://ror.org/02w42ss30 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I29770179 |
| authorships[0].institutions[0].country_code | HU |
| authorships[0].institutions[0].display_name | Budapest University of Technology and Economics |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Szilvia Lestyan |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | CrySyS Lab, Dept. of Networked Systems and Services, Budapest Univ. of Technology and Economics and Hungary, --- Select a Country --- |
| authorships[1].author.id | https://openalex.org/A5047386875 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-4437-0110 |
| authorships[1].author.display_name | Gergely Ács |
| authorships[1].countries | HU |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I29770179 |
| authorships[1].affiliations[0].raw_affiliation_string | CrySyS Lab, Dept. of Networked Systems and Services, Budapest Univ. of Technology and Economics and Hungary, --- Select a Country --- |
| authorships[1].institutions[0].id | https://openalex.org/I29770179 |
| authorships[1].institutions[0].ror | https://ror.org/02w42ss30 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I29770179 |
| authorships[1].institutions[0].country_code | HU |
| authorships[1].institutions[0].display_name | Budapest University of Technology and Economics |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Gergely Acs |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | CrySyS Lab, Dept. of Networked Systems and Services, Budapest Univ. of Technology and Economics and Hungary, --- Select a Country --- |
| authorships[2].author.id | https://openalex.org/A5002026615 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-3891-3855 |
| authorships[2].author.display_name | Gergely Biczók |
| authorships[2].countries | HU |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I29770179 |
| authorships[2].affiliations[0].raw_affiliation_string | CrySyS Lab, Dept. of Networked Systems and Services, Budapest Univ. of Technology and Economics and Hungary, --- Select a Country --- |
| authorships[2].institutions[0].id | https://openalex.org/I29770179 |
| authorships[2].institutions[0].ror | https://ror.org/02w42ss30 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I29770179 |
| authorships[2].institutions[0].country_code | HU |
| authorships[2].institutions[0].display_name | Budapest University of Technology and Economics |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Gergely Biczok |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | CrySyS Lab, Dept. of Networked Systems and Services, Budapest Univ. of Technology and Economics and Hungary, --- Select a Country --- |
| authorships[3].author.id | https://openalex.org/A5087280793 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-6172-5772 |
| authorships[3].author.display_name | Zsolt Szalay |
| authorships[3].countries | HU |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I29770179 |
| authorships[3].affiliations[0].raw_affiliation_string | Budapest University of Technology and Economics |
| authorships[3].institutions[0].id | https://openalex.org/I29770179 |
| authorships[3].institutions[0].ror | https://ror.org/02w42ss30 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I29770179 |
| authorships[3].institutions[0].country_code | HU |
| authorships[3].institutions[0].display_name | Budapest University of Technology and Economics |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Zsolt Szalay |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Budapest University of Technology and Economics |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/1902.08956 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Extracting vehicle sensor signals from CAN logs for driver re-identification |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T11099 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9993000030517578 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2203 |
| primary_topic.subfield.display_name | Automotive Engineering |
| primary_topic.display_name | Autonomous Vehicle Technology and Safety |
| related_works | https://openalex.org/W2916772464, https://openalex.org/W2754552138, https://openalex.org/W3176364128, https://openalex.org/W3078148541, https://openalex.org/W2199299436, https://openalex.org/W1481217178, https://openalex.org/W2973180979, https://openalex.org/W2967685168, https://openalex.org/W2499234011, https://openalex.org/W2606851531, https://openalex.org/W1785422837, https://openalex.org/W3109042733, https://openalex.org/W2113602963, https://openalex.org/W3210678009, https://openalex.org/W2189449838, https://openalex.org/W2005858952, https://openalex.org/W2620439505, https://openalex.org/W2952168212, https://openalex.org/W2912562035, https://openalex.org/W2749050329 |
| cited_by_count | 2 |
| counts_by_year[0].year | 2020 |
| counts_by_year[0].cited_by_count | 2 |
| locations_count | 3 |
| best_oa_location.id | pmh:oai:arXiv.org:1902.08956 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/1902.08956 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/1902.08956 |
| primary_location.id | pmh:oai:arXiv.org:1902.08956 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/1902.08956 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/1902.08956 |
| publication_date | 2019-02-24 |
| publication_year | 2019 |
| referenced_works | https://openalex.org/W1785021972, https://openalex.org/W2792204436, https://openalex.org/W2592742990, https://openalex.org/W1972441921, https://openalex.org/W2963535483, https://openalex.org/W2964092202, https://openalex.org/W2286343943, https://openalex.org/W1584662422, https://openalex.org/W2761751533, https://openalex.org/W2144994235 |
| referenced_works_count | 10 |
| abstract_inverted_index.a | 26, 83, 161 |
| abstract_inverted_index.33 | 164 |
| abstract_inverted_index.We | 116, 146 |
| abstract_inverted_index.as | 183 |
| abstract_inverted_index.be | 106, 127, 154, 181 |
| abstract_inverted_index.do | 90 |
| abstract_inverted_index.in | 160, 171 |
| abstract_inverted_index.is | 1, 80 |
| abstract_inverted_index.of | 29, 40, 59, 163, 186 |
| abstract_inverted_index.on | 49 |
| abstract_inverted_index.or | 67 |
| abstract_inverted_index.se | 175 |
| abstract_inverted_index.to | 25, 132, 140, 157, 180 |
| abstract_inverted_index.we | 101 |
| abstract_inverted_index.CAN | 52, 77, 98, 110, 144, 172 |
| abstract_inverted_index.all | 58 |
| abstract_inverted_index.and | 17, 129 |
| abstract_inverted_index.are | 15 |
| abstract_inverted_index.can | 105, 126, 153 |
| abstract_inverted_index.car | 7, 87 |
| abstract_inverted_index.for | 5 |
| abstract_inverted_index.how | 13 |
| abstract_inverted_index.is, | 139 |
| abstract_inverted_index.log | 78 |
| abstract_inverted_index.new | 3 |
| abstract_inverted_index.not | 82, 91, 167, 177 |
| abstract_inverted_index.oil | 4 |
| abstract_inverted_index.per | 174 |
| abstract_inverted_index.the | 2, 6, 20, 38, 44, 50, 76, 93, 143, 150 |
| abstract_inverted_index.way | 28 |
| abstract_inverted_index.Area | 54 |
| abstract_inverted_index.Cars | 9 |
| abstract_inverted_index.Data | 0 |
| abstract_inverted_index.also | 147 |
| abstract_inverted_index.been | 73 |
| abstract_inverted_index.bus. | 56 |
| abstract_inverted_index.data | 11, 47, 185 |
| abstract_inverted_index.does | 176 |
| abstract_inverted_index.from | 75, 109 |
| abstract_inverted_index.have | 35, 71, 120 |
| abstract_inverted_index.logs | 111, 173 |
| abstract_inverted_index.rise | 24 |
| abstract_inverted_index.show | 102 |
| abstract_inverted_index.that | 70, 103, 118, 138, 149 |
| abstract_inverted_index.them | 60, 134, 179 |
| abstract_inverted_index.they | 14 |
| abstract_inverted_index.used | 16, 61, 131, 156 |
| abstract_inverted_index.about | 12 |
| abstract_inverted_index.brake | 65 |
| abstract_inverted_index.exact | 94 |
| abstract_inverted_index.gives | 23 |
| abstract_inverted_index.logs. | 99 |
| abstract_inverted_index.novel | 27 |
| abstract_inverted_index.pedal | 66 |
| abstract_inverted_index.prior | 33 |
| abstract_inverted_index.quasi | 141 |
| abstract_inverted_index.using | 43, 112 |
| abstract_inverted_index.wheel | 21 |
| abstract_inverted_index.which | 22, 79, 125 |
| abstract_inverted_index.who's | 18 |
| abstract_inverted_index.works | 34 |
| abstract_inverted_index.(e.g., | 63 |
| abstract_inverted_index.across | 135 |
| abstract_inverted_index.behind | 19 |
| abstract_inverted_index.driver | 41 |
| abstract_inverted_index.itself | 81 |
| abstract_inverted_index.learnt | 128 |
| abstract_inverted_index.reveal | 92 |
| abstract_inverted_index.signal | 95, 169 |
| abstract_inverted_index.within | 97 |
| abstract_inverted_index.Indeed, | 86 |
| abstract_inverted_index.Several | 32 |
| abstract_inverted_index.already | 72 |
| abstract_inverted_index.dataset | 162 |
| abstract_inverted_index.exploit | 117 |
| abstract_inverted_index.machine | 113 |
| abstract_inverted_index.network | 46 |
| abstract_inverted_index.prevent | 178 |
| abstract_inverted_index.several | 121 |
| abstract_inverted_index.signals | 62, 104, 119, 152 |
| abstract_inverted_index.However, | 57 |
| abstract_inverted_index.Network) | 55 |
| abstract_inverted_index.captured | 48 |
| abstract_inverted_index.drivers. | 165, 187 |
| abstract_inverted_index.features | 124 |
| abstract_inverted_index.generate | 10 |
| abstract_inverted_index.identify | 133 |
| abstract_inverted_index.learning | 114 |
| abstract_inverted_index.location | 96 |
| abstract_inverted_index.personal | 184 |
| abstract_inverted_index.process. | 85 |
| abstract_inverted_index.regarded | 182 |
| abstract_inverted_index.different | 136 |
| abstract_inverted_index.extracted | 74, 108, 151 |
| abstract_inverted_index.industry. | 8 |
| abstract_inverted_index.locations | 170 |
| abstract_inverted_index.position) | 69 |
| abstract_inverted_index.profiling | 30 |
| abstract_inverted_index.protocol. | 145 |
| abstract_inverted_index.revealing | 168 |
| abstract_inverted_index.vehicle's | 51 |
| abstract_inverted_index.vehicles, | 137 |
| abstract_inverted_index.velocity, | 64 |
| abstract_inverted_index.Therefore, | 166 |
| abstract_inverted_index.in-vehicle | 45 |
| abstract_inverted_index.(Controller | 53 |
| abstract_inverted_index.accelerator | 68 |
| abstract_inverted_index.demonstrate | 148 |
| abstract_inverted_index.effectively | 130 |
| abstract_inverted_index.efficiently | 107 |
| abstract_inverted_index.feasibility | 39 |
| abstract_inverted_index.individuals | 159 |
| abstract_inverted_index.re-identify | 158 |
| abstract_inverted_index.statistical | 123 |
| abstract_inverted_index.techniques. | 115 |
| abstract_inverted_index.demonstrated | 37 |
| abstract_inverted_index.individuals. | 31 |
| abstract_inverted_index.successfully | 36, 155 |
| abstract_inverted_index.Nevertheless, | 100 |
| abstract_inverted_index.intentionally | 89 |
| abstract_inverted_index.manufacturers | 88 |
| abstract_inverted_index.distinguishing | 122 |
| abstract_inverted_index.straightforward | 84 |
| abstract_inverted_index.re-identification | 42 |
| abstract_inverted_index."reverse-engineer" | 142 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5600000023841858 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |