eprintid: 10194561 rev_number: 10 eprint_status: archive userid: 699 dir: disk0/10/19/45/61 datestamp: 2024-07-16 13:32:24 lastmod: 2024-12-19 11:09:56 status_changed: 2024-07-16 13:32:24 type: proceedings_section metadata_visibility: show sword_depositor: 699 creators_name: Perez, Beatrice creators_name: Mehrotra, Abhinav creators_name: Musolesi, Mirco title: MarcoPolo: A Zero-Permission Attack for Location Type Inference from the Magnetic Field Using Mobile Devices ispublished: pub divisions: UCL divisions: B04 divisions: F48 note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. abstract: Location information extracted from mobile devices has been largely exploited to reveal our routines, significant places, and interests, just to name a few. Given the sensitivity of the information it reveals, location access is protected by mobile operating systems and users have control over which applications can access it. We argue that applications can still infer the coarse-grain location information by using alternative sensors that are available in off-the-shelf mobile devices that do not require any permissions from the users. In this paper we present a zero-permission attack based on the use of the in-built magnetometer, considering a variety of methods for identifying location-types from their magnetic signature. We implement the proposed approach by using four different techniques for time-series classification. In order to evaluate the approach, we conduct an in-the-wild study to collect a dataset of nearly 70 h of magnetometer readings with six different phones at 66 locations, each accompanied by a label that classifies it as belonging to one of six selected categories. Finally, using this dataset, we quantify the performance of all models based on two evaluation criteria: (i) leave-a-place-out (using the test data collected from an unknown place), and (ii) leave-a-device-out (using the test data collected from an unknown device) showing that we are able to achieve 40.5% and 39.5% accuracy in classifying the location-type for each evaluation criteria respectively against a random baseline of approximately 16.7% for both of them. date: 2024-09-29 date_type: published publisher: Springer official_url: https://doi.org/10.1007/978-981-97-8016-7_1 full_text_type: other language: eng verified: verified_manual elements_id: 2296936 doi: 10.1007/978-981-97-8016-7_1 isbn_13: 978-981-97-8015-0 lyricists_name: Musolesi, Mirco lyricists_id: MMUSO05 actors_name: Musolesi, Mirco actors_id: MMUSO05 actors_role: owner full_text_status: restricted pres_type: paper series: Lecture Notes in Computer Science (LNCS) volume: 14906 place_of_pub: Singapore pagerange: 3-24 event_title: 23rd International Conference on Cryptology And Network Security (CANS 2024) issn: 0302-9743 book_title: Cryptology and Network Security: 23rd International Conference, CANS 2024, Cambridge, UK, September 24–27, 2024, Proceedings, Part II editors_name: Kohlweiss, Markulf editors_name: Di Pietro, Roberto editors_name: Beresford, Alastair citation: Perez, Beatrice; Mehrotra, Abhinav; Musolesi, Mirco; (2024) MarcoPolo: A Zero-Permission Attack for Location Type Inference from the Magnetic Field Using Mobile Devices. In: Kohlweiss, Markulf and Di Pietro, Roberto and Beresford, Alastair, (eds.) Cryptology and Network Security: 23rd International Conference, CANS 2024, Cambridge, UK, September 24–27, 2024, Proceedings, Part II. (pp. pp. 3-24). Springer: Singapore. document_url: https://discovery.ucl.ac.uk/id/eprint/10194561/1/cans2024_marcopolo.pdf