{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:02:10Z","timestamp":1750309330899,"version":"3.41.0"},"reference-count":24,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0\/"}],"funder":[{"name":"Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) as part of the Research and Training Group 2475 \u201cCybercrime and Forensic Computing\u201d","award":["393541319\/GRK2475\/2-2024"],"award-info":[{"award-number":["393541319\/GRK2475\/2-2024"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Digital Threats"],"published-print":{"date-parts":[[2024,9,30]]},"abstract":"<jats:p>\n            Geolocation data recorded by consumer electronics is usually considered very helpful in criminal investigations: Every few steps, every few seconds, the location of a suspect, victim, witness, or others can be pinpointed as it was automatically recorded in the background. Compared to the commonly used cell tower location data, device-local data from a\n            <jats:italic>global navigation satellite system<\/jats:italic>\n            (GNSS) has far higher precision (both spatial and temporal), but suffers from a lack of trust, because data provenance is under potential control of the user. In this article, we propose two approaches of validating and analyzing such data with high confidence. The first approach formulates and checks internal consistency criteria of GNSS data, while the second approach also takes external data sources about the surrounding environment in the form of OpenStreetMap data into account. In both approaches, we formalize the concept of a\n            <jats:italic>data anomaly<\/jats:italic>\n            and argue that an absence of anomalies implies more trustworthy data and thus higher evidential value. This way, the vast information contained in high-density location data may actually lead to more detailed insights instead of only increasing data noise in investigations.\n          <\/jats:p>","DOI":"10.1145\/3688809","type":"journal-article","created":{"date-parts":[[2024,8,20]],"date-time":"2024-08-20T14:51:20Z","timestamp":1724165480000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Increasing Street Credibility: Cartography-Aware Forensic Analysis of GNSS Trace Validity"],"prefix":"10.1145","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5397-1685","authenticated-orcid":false,"given":"Andreas","family":"Hammer","sequence":"first","affiliation":[{"name":"IT Security Infrastructures Lab, Friedrich-Alexander Universit\u00e4t Erlangen-N\u00fcrnberg (FAU), Erlangen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7117-6609","authenticated-orcid":false,"given":"Christian","family":"Cerny","sequence":"additional","affiliation":[{"name":"IT Security Infrastructures Lab, Friedrich-Alexander Universit\u00e4t Erlangen-N\u00fcrnberg (FAU), Erlangen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2942-6206","authenticated-orcid":false,"given":"Christoph","family":"Jung","sequence":"additional","affiliation":[{"name":"IT Security Infrastructures Lab, Friedrich-Alexander Universit\u00e4t Erlangen-N\u00fcrnberg (FAU), Erlangen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1077-7537","authenticated-orcid":false,"given":"Christian","family":"Eichenm\u00fcller","sequence":"additional","affiliation":[{"name":"IT Security Infrastructures Lab, Friedrich-Alexander Universit\u00e4t Erlangen-N\u00fcrnberg (FAU), Erlangen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8279-8401","authenticated-orcid":false,"given":"Felix","family":"Freiling","sequence":"additional","affiliation":[{"name":"IT Security Infrastructures Lab, Friedrich-Alexander Universit\u00e4t Erlangen-N\u00fcrnberg (FAU), Erlangen, Germany"}]}],"member":"320","published-online":{"date-parts":[[2024,10,26]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Defense Mapping Agency. 1991. Department of Defense World Geodetic System 1984: Its Definition and Relationships with Local Geodetic Systems. Defense Technical Information Center (1991). Retrieved from https:\/\/apps.dtic.mil\/sti\/citations\/ADA280358"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi7080323"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1179\/1743277414Y.0000000085"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1111\/tgis.12073"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.fsidi.2020.300928"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.diin.2018.12.004"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.3846\/20296991.2015.1160493"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.21236\/ADA562672"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.fsidi.2020.301009"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-14280-7_8"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","unstructured":"A. Yair Grinberger Marco Minghini Levente Juh\u00e1sz Godwin Yeboah and Peter Mooney. 2022. OSM Science\u2014The Academic Study of the OpenStreetMap Project Data Contributors Community and Applications ISPRS Int. J. Geo-Inf 11 4 (2022) 230. DOI: 10.3390\/ijgi11040230","DOI":"10.3390\/ijgi11040230"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1111\/tgis.12746"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.20378\/irb-58426"},{"key":"e_1_3_2_15_2","unstructured":"Trimble Inc. 2024. Trimble BD992-INS \u2014 OEMGNSS. Retrieved February 2024 from https:\/\/oemgnss.trimble.com\/en\/products\/receiver-modules\/bd992-ins"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.192.4246.1293"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/19.278607"},{"key":"e_1_3_2_18_2","first-page":"75","article-title":"Raum und Zeit","volume":"18","author":"Minkowski Hermann","year":"1909","unstructured":"Hermann Minkowski. 1909. Raum und Zeit. Jahresberichte der Deutschen Mathematiker-Vereinigung 18 (1909), 75\u201388.","journal-title":"Jahresberichte der Deutschen Mathematiker-Vereinigung"},{"issue":"2","key":"e_1_3_2_19_2","first-page":"298","article-title":"Landmark Based Shortest Path Detection by Using A* Algorithm and Haversine Formula","volume":"1","author":"Nichat Mangesh","year":"2013","unstructured":"Mangesh Nichat and Nitin R. Chopde. 2013. Landmark Based Shortest Path Detection by Using A* Algorithm and Haversine Formula. International Journal of Innovative Research in Computer and Communication Engineering 1, 2 (2013), 298\u2013302.","journal-title":"International Journal of Innovative Research in Computer and Communication Engineering"},{"key":"e_1_3_2_20_2","unstructured":"OSM Imports 2023. OpenStreetMap - Import\/Catalogue. Retrieved from https:\/\/wiki.openstreetmap.org\/w\/index.php?title=Import\/Catalogue{&}oldid=2622323"},{"key":"e_1_3_2_21_2","unstructured":"Matthias Plennert Georg Glasze and Christoph Schlieder. 2019. The Socio-Technical Background of an Unconventional Software Architecture in OpenStreetMap: Understanding the Implementation of \u2018Folksonomy\u2019. Computational Culture 7 (2019). Retrieved from http:\/\/computationalculture.net\/the-socio-technical-background-of-an-unconventional-software-architecture-in-openstreetmap-understanding-the-implementation-of-folksonomy\/"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1080\/20961790.2018.1509187"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1179\/003962611X12894696204948"},{"key":"e_1_3_2_24_2","first-page":"98","volume-title":"13th Australian Digital Forensics Conference, held from the 30 November \u2013 2 December, 2015","author":"Sansurooah Krishnun","year":"2018","unstructured":"Krishnun Sansurooah and Bradley Keane. 2018. The spy in your pocket: Smartphones and geo-location data. 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