{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T13:13:45Z","timestamp":1779974025514,"version":"3.53.1"},"reference-count":77,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T00:00:00Z","timestamp":1779926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Hum.-Comput. Interact."],"published-print":{"date-parts":[[2026,5,31]]},"abstract":"<jats:p>With the growing use of eye tracking on VR and mobile platforms, gaze data is increasing. While scanpath comparison is important to gaze behavior analysis, existing methods lack privacy-preserving capabilities for real-world use. We present a garbled-circuit (GC)-based approach enabling secure storage and privacy-preserving scanpath comparison under the semi-honest model. It supports two configurations: (1) a two-party setting where the data owner and processor jointly compute similarity scores without revealing their inputs, and (2) a server-assisted setting where encrypted scanpaths are stored and processed while the data owner remains offline. All decryption and comparison operations are executed inside the GC. Experiments on three eye-tracking datasets evaluate fidelity, runtime, and communication, and show secure results for MultiMatch, ScanMatch, and SubsMatch closely match plaintext outcomes, with manageable runtime and communication overhead. Tests under various network conditions indicate that the design remains feasible for real-world privacy-preserving scanpath analysis and can be extended to other GC-based behavioral algorithms.<\/jats:p>","DOI":"10.1145\/3806022","type":"journal-article","created":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T12:44:33Z","timestamp":1779972273000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Secure Storage and Privacy-Preserving Scanpath Comparison via Garbled Circuits in Eye Tracking ETRA008"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3390-6154","authenticated-orcid":false,"given":"Suleyman","family":"Ozdel","sequence":"first","affiliation":[{"name":"Human-Centered Technologies for Learning","place":["Munich, Germany"]},{"name":"Technical University of Munich","place":["Munich, Germany"]},{"name":"Munich Center for Machine Learning","place":["Munich, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0634-1475","authenticated-orcid":false,"given":"Amr","family":"Nader","sequence":"additional","affiliation":[{"name":"Human-Centered Technologies for Learning","place":["M\u00fcnchen, Germany"]},{"name":"Technical University of Munich","place":["M\u00fcnchen, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8895-4997","authenticated-orcid":false,"given":"Yasmeen","family":"Abdrabou","sequence":"additional","affiliation":[{"name":"Human-Centered Technologies for Learning","place":["Munich, Germany"]},{"name":"Technical University of Munich","place":["Munich, Germany"]},{"name":"Munich Center for Machine Learning","place":["Munich, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3146-4484","authenticated-orcid":false,"given":"Enkelejda","family":"Kasneci","sequence":"additional","affiliation":[{"name":"Human-Centered Technologies for Learning","place":["Munich, Germany"]},{"name":"Technical University of Munich","place":["Munich, Germany"]},{"name":"Munich Center for Machine Learning","place":["Munich, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,5,28]]},"reference":[{"key":"e_1_3_3_2_1","volume-title":"Cryptographic Mechanisms: Recommendations and Key Lengths, BSI TR-02102-1","year":"2025","unstructured":"2025. Cryptographic Mechanisms: Recommendations and Key Lengths, BSI TR-02102-1. Technical Report. Federal Office for Information Security (BSI). https:\/\/www.bsi.bund.de\/SharedDocs\/Downloads\/EN\/BSI\/Publications\/TechGuidelines\/TG02102\/BSI-TR-02102-1.pdf?__blob=publicationFile&v=9"},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3715669.3726788"},{"key":"e_1_3_3_4_1","doi-asserted-by":"crossref","unstructured":"Isayas\u00a0Berhe Adhanom Paul MacNeilage and Eelke Folmer. 2023. Eye tracking in virtual reality: a broad review of applications and challenges. Virtual Reality 27 2 (2023) 1481\u20131505.","DOI":"10.1007\/s10055-022-00738-z"},{"key":"e_1_3_3_5_1","volume-title":"360EM: Ground-truth eye movement dataset for 360-degree videos","author":"Agtzidis Ioannis","year":"2019","unstructured":"Ioannis Agtzidis, Mikhail Startsev, and Michael Dorr. 2019a. 360EM: Ground-truth eye movement dataset for 360-degree videos. https:\/\/gin.g-node.org\/ioannis.agtzidis\/360_em_dataset Accessed: 2026-01-23."},{"key":"e_1_3_3_6_1","unstructured":"Ioannis Agtzidis Mikhail Startsev and Michael Dorr. 2019b. A ground-truth data set and a classification algorithm for eye movements in 360-degree videos. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1903.06474 (2019)."},{"key":"e_1_3_3_7_1","doi-asserted-by":"crossref","unstructured":"Nicola\u00a0C Anderson Fraser Anderson Alan Kingstone and Walter\u00a0F Bischof. 2015. A comparison of scanpath comparison methods. Behavior research methods 47 (2015) 1377\u20131392.","DOI":"10.3758\/s13428-014-0550-3"},{"key":"e_1_3_3_8_1","doi-asserted-by":"crossref","unstructured":"Nicola\u00a0C Anderson Walter\u00a0F Bischof Kaitlin\u00a0EW Laidlaw Evan\u00a0F Risko and Alan Kingstone. 2013. Recurrence quantification analysis of eye movements. Behavior research methods 45 3 (2013) 842\u2013856.","DOI":"10.3758\/s13428-012-0299-5"},{"key":"e_1_3_3_9_1","doi-asserted-by":"crossref","unstructured":"Gennady Andrienko Natalia Andrienko Michael Burch and Daniel Weiskopf. 2012. Visual analytics methodology for eye movement studies. IEEE transactions on Visualization and Computer Graphics 18 12 (2012) 2889\u20132898.","DOI":"10.1109\/TVCG.2012.276"},{"key":"e_1_3_3_10_1","doi-asserted-by":"publisher","DOI":"10.6028\/NIST.SP.800-131Ar3.ipd"},{"key":"e_1_3_3_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2382196.2382279"},{"key":"e_1_3_3_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3335741.3335756"},{"key":"e_1_3_3_13_1","doi-asserted-by":"crossref","unstructured":"Sophie\u00a0C Boerman and C\u00e9line\u00a0M M\u00fcller. 2022. Understanding which cues people use to identify influencer marketing on Instagram: an eye tracking study and experiment. International Journal of Advertising 41 1 (2022) 6\u201329.","DOI":"10.1080\/02650487.2021.1986256"},{"key":"e_1_3_3_14_1","doi-asserted-by":"crossref","unstructured":"Ali Borji and Laurent Itti. 2014. Defending Yarbus: Eye movements reveal observers\u2019 task. Journal of vision 14 3 (2014) 29\u201329.","DOI":"10.1167\/14.3.29"},{"key":"e_1_3_3_15_1","doi-asserted-by":"publisher","unstructured":"Efe Bozkir Onur G\u00fcnl\u00fc Wolfgang Fuhl Rafael\u00a0F. Schaefer and Enkelejda Kasneci. 2021. Differential privacy for eye tracking with temporal correlations. PLOS ONE 16 8 (2021) 1\u201322. doi:10.1371\/journal.pone.0255979","DOI":"10.1371\/journal.pone.0255979"},{"key":"e_1_3_3_16_1","unstructured":"Efe Bozkir Suleyman Ozdel Mengdi Wang Brendan David-John Hong Gao Kevin Butler Eakta Jain and Enkelejda Kasneci. 2023. Eye-tracked virtual reality: A comprehensive survey on methods and privacy challenges. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2305.14080 (2023)."},{"key":"e_1_3_3_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3379156.3391364"},{"key":"e_1_3_3_18_1","doi-asserted-by":"publisher","DOI":"10.4324\/9781315160559-3"},{"key":"e_1_3_3_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3204493.3204550"},{"key":"e_1_3_3_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3379155.3391320"},{"key":"e_1_3_3_21_1","doi-asserted-by":"crossref","unstructured":"Meia Chita-Tegmark. 2016. Social attention in ASD: A review and meta-analysis of eye-tracking studies. Research in developmental disabilities 48 (2016) 79\u201393.","DOI":"10.1016\/j.ridd.2015.10.011"},{"key":"e_1_3_3_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-76900-2_18"},{"key":"e_1_3_3_23_1","doi-asserted-by":"crossref","unstructured":"Filipe Cristino Sebastiaan Math\u00f4t Jan Theeuwes and Iain\u00a0D Gilchrist. 2010. ScanMatch: A novel method for comparing fixation sequences. Behavior research methods 42 (2010) 692\u2013700.","DOI":"10.3758\/BRM.42.3.692"},{"key":"e_1_3_3_24_1","unstructured":"Joan Daemen and Vincent Rijmen. 1999. AES proposal: Rijndael. (1999)."},{"key":"e_1_3_3_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-32009-5_38"},{"key":"e_1_3_3_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3517031.3529618"},{"key":"e_1_3_3_27_1","doi-asserted-by":"publisher","unstructured":"Brendan David-John Kevin Butler and Eakta Jain. 2023. Privacy-preserving datasets of eye-tracking samples with applications in XR. IEEE Transactions on Visualization and Computer Graphics 29 5 (2023) 2774\u20132784. doi:10.1109\/TVCG.2023.3247048","DOI":"10.1109\/TVCG.2023.3247048"},{"key":"e_1_3_3_28_1","doi-asserted-by":"crossref","unstructured":"Brendan David-John Diane Hosfelt Kevin Butler and Eakta Jain. 2021. A privacy-preserving approach to streaming eye-tracking data. IEEE Transactions on Visualization and Computer Graphics 27 5 (2021) 2555\u20132565.","DOI":"10.1109\/TVCG.2021.3067787"},{"key":"e_1_3_3_29_1","doi-asserted-by":"publisher","unstructured":"Richard Dewhurst Marcus Nystr\u00f6m Halszka Jarodzka Tom Foulsham Roger Johansson and Kenneth Holmqvist. 2012. It depends on how you look at it: Scanpath comparison in multiple dimensions with MultiMatch a vector-based approach. Behavior Research Methods 44 4 (2012) 1079\u20131100. doi:10.3758\/s13428-012-0212-2","DOI":"10.3758\/s13428-012-0212-2"},{"key":"e_1_3_3_30_1","doi-asserted-by":"crossref","unstructured":"Morris Dworkin. 2001. Recommendation for block cipher modes of operation. NIST special publication 800 (2001) 38B.","DOI":"10.6028\/NIST.SP.800-38a"},{"key":"e_1_3_3_31_1","series-title":"Proceedings of Machine Learning Research","first-page":"20","volume-title":"Proceedings of The 1st Gaze Meets ML workshop","author":"Elfares Mayar","year":"2023","unstructured":"Mayar Elfares, Zhiming Hu, Pascal Reisert, Andreas Bulling, and Ralf K\u00fcsters. 2023. Federated Learning for Appearance-based Gaze Estimation in the Wild. In Proceedings of The 1st Gaze Meets ML workshop(Proceedings of Machine Learning Research, Vol.\u00a0210). PMLR, 20\u201336. https:\/\/proceedings.mlr.press\/v210\/elfares23a.html"},{"key":"e_1_3_3_32_1","doi-asserted-by":"crossref","unstructured":"Mayar Elfares Pascal Reisert Zhiming Hu Wenwu Tang Ralf K\u00fcsters and Andreas Bulling. 2024. PrivatEyes: appearance-based gaze estimation using federated secure multi-party computation. Proceedings of the ACM on Human-Computer Interaction 8 ETRA (2024) 1\u201323.","DOI":"10.1145\/3655606"},{"key":"e_1_3_3_33_1","unstructured":"Mayar Elfares Pascal Reisert Ralf K\u00fcsters and Andreas Bulling. 2025. QualitEye: Public and Privacy-preserving Gaze Data Quality Verification. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2506.05908 (2025)."},{"key":"e_1_3_3_34_1","doi-asserted-by":"publisher","unstructured":"David Evans Vladimir Kolesnikov and Mike Rosulek. 2018. A Pragmatic Introduction to Secure Multi-Party Computation. Found. Trends Priv. Secur. 2 2\u20133 (Dec. 2018) 70\u2013246. doi:10.1561\/3300000019","DOI":"10.1561\/3300000019"},{"key":"e_1_3_3_35_1","doi-asserted-by":"crossref","unstructured":"Shimon Even Oded Goldreich and Abraham Lempel. 1985. A randomized protocol for signing contracts. Commun. ACM 28 6 (1985) 637\u2013647.","DOI":"10.1145\/3812.3818"},{"key":"e_1_3_3_36_1","doi-asserted-by":"crossref","unstructured":"Tom Foulsham Richard Dewhurst Marcus Nystr\u00f6m Halszka Jarodzka Roger Johansson Geoffrey Underwood and Kenneth Holmqvist. 2012. Comparing scanpaths during scene encoding and recognition: A multi-dimensional approach. Journal of Eye Movement Research 5 4 (2012).","DOI":"10.16910\/jemr.5.4.3"},{"key":"e_1_3_3_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86380-7_48"},{"key":"e_1_3_3_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3338466.3358924"},{"key":"e_1_3_3_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/28395.28420"},{"key":"e_1_3_3_40_1","doi-asserted-by":"publisher","unstructured":"Reiko Graham Alison Hoover Natalie\u00a0A. Ceballos and Oleg Komogortsev. 2011. Body mass index moderates gaze orienting biases and pupil diameter to high and low calorie food images. Appetite 56 3 (2011) 577\u2013586. doi:10.1016\/j.appet.2011.01.029","DOI":"10.1016\/j.appet.2011.01.029"},{"key":"e_1_3_3_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3379157.3390511"},{"key":"e_1_3_3_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCB.2011.6117536"},{"key":"e_1_3_3_43_1","volume-title":"EHTask Dataset: Eye and head movement recordings for task recognition in immersive VR","author":"Hu Zhiming","year":"2021","unstructured":"Zhiming Hu, Andreas Bulling, Sheng Li, and Guoping Wang. 2021a. EHTask Dataset: Eye and head movement recordings for task recognition in immersive VR. https:\/\/zhiminghu.net\/hu22_ehtask.html Accessed: 2026-01-23."},{"key":"e_1_3_3_44_1","unstructured":"Zhiming Hu Andreas Bulling Sheng Li and Guoping Wang. 2021b. Ehtask: Recognizing user tasks from eye and head movements in immersive virtual reality. IEEE Transactions on Visualization and Computer Graphics (2021)."},{"key":"e_1_3_3_45_1","volume-title":"20th USENIX Security Symposium (USENIX Security 11)","author":"Huang Yan","year":"2011","unstructured":"Yan Huang, David Evans, Jonathan Katz, and Lior Malka. 2011. Faster secure { Two-Party} computation using garbled circuits. In 20th USENIX Security Symposium (USENIX Security 11)."},{"key":"e_1_3_3_46_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-45146-4_9"},{"key":"e_1_3_3_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/1743666.1743718"},{"key":"e_1_3_3_48_1","doi-asserted-by":"publisher","DOI":"10.1007\/11555827_23"},{"key":"e_1_3_3_49_1","doi-asserted-by":"publisher","DOI":"10.5555\/1894863.1894876"},{"key":"e_1_3_3_50_1","doi-asserted-by":"crossref","unstructured":"Fengfeng Ke Ruohan Liu Zlatko Sokolikj Ibrahim Dahlstrom-Hakki and Maya Israel. 2024. Using eye-tracking in education: review of empirical research and technology. Educational technology research and development 72 3 (2024) 1383\u20131418.","DOI":"10.1007\/s11423-024-10342-4"},{"key":"e_1_3_3_51_1","doi-asserted-by":"crossref","unstructured":"Krzysztof Krejtz Andrew\u00a0T Duchowski Anna Niedzielska Cezary Biele and Izabela Krejtz. 2018. Eye tracking cognitive load using pupil diameter and microsaccades with fixed gaze. PloS one 13 9 (2018) e0203629.","DOI":"10.1371\/journal.pone.0203629"},{"key":"e_1_3_3_52_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-42504-3_15"},{"key":"e_1_3_3_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/2578153.2578206"},{"key":"e_1_3_3_54_1","volume-title":"USENIX Security Symposium","author":"Li Jingjie","year":"2021","unstructured":"Jingjie Li, Amrita\u00a0Roy Chowdhury, Kassem Fawaz, and Younghyun Kim. 2021. Kal\u03f5 ido: Real-Time Privacy Control for Eye-Tracking Systems. In USENIX Security Symposium. USENIX Association."},{"key":"e_1_3_3_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/2638728.2641688"},{"key":"e_1_3_3_56_1","doi-asserted-by":"crossref","unstructured":"Yehuda Lindell and Benny Pinkas. 2009. A proof of security of Yao\u2019s protocol for two-party computation. Journal of cryptology 22 2 (2009) 161\u2013188.","DOI":"10.1007\/s00145-008-9036-8"},{"key":"e_1_3_3_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3314111.3319823"},{"key":"e_1_3_3_58_1","doi-asserted-by":"crossref","unstructured":"Nathan Manohar Abhishek Jain and Amit Sahai. 2020. Self-processing private sensor data via garbled encryption. Proceedings on Privacy Enhancing Technologies (2020).","DOI":"10.2478\/popets-2020-0081"},{"key":"e_1_3_3_59_1","doi-asserted-by":"crossref","unstructured":"Saul\u00a0B Needleman and Christian\u00a0D Wunsch. 1970. A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of molecular biology 48 3 (1970) 443\u2013453.","DOI":"10.1016\/0022-2836(70)90057-4"},{"key":"e_1_3_3_60_1","doi-asserted-by":"crossref","unstructured":"Jakub\u00a0\u0160t\u011bp\u00e1n Nov\u00e1k Jan Masner Petr Benda Pavel \u0160imek and Vojt\u011bch Merunka. 2024. Eye tracking usability and user experience: A systematic review. International Journal of Human\u2013Computer Interaction 40 17 (2024) 4484\u20134500.","DOI":"10.1080\/10447318.2023.2221600"},{"key":"e_1_3_3_61_1","doi-asserted-by":"crossref","unstructured":"Marcus Nystr\u00f6m and Kenneth Holmqvist. 2010. An adaptive algorithm for fixation saccade and glissade detection in eyetracking data. Behavior research methods 42 1 (2010) 188\u2013204.","DOI":"10.3758\/BRM.42.1.188"},{"key":"e_1_3_3_62_1","doi-asserted-by":"publisher","unstructured":"Suleyman Ozdel Efe Bozkir and Enkelejda Kasneci. 2024. Privacy-preserving scanpath comparison for pervasive eye tracking. Proceedings of the ACM on Human-Computer Interaction 8 ETRA (2024) 1\u201328. doi:10.1145\/3655605","DOI":"10.1145\/3655605"},{"key":"e_1_3_3_63_1","unstructured":"Suleyman Ozdel Can Sarpkaya Efe Bozkir Hong Gao and Enkelejda Kasneci. 2025. Examining the Role of LLM-Driven Interactions on Attention and Cognitive Engagement in Virtual Classrooms. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2505.07377 (2025)."},{"key":"e_1_3_3_64_1","doi-asserted-by":"publisher","DOI":"10.5555\/AAI28263314"},{"key":"e_1_3_3_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/3083187.3083218"},{"key":"e_1_3_3_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/QoMEX.2017.7965659"},{"key":"e_1_3_3_67_1","volume-title":"Salient360: Head and eye movement dataset for omnidirectional content","author":"Rai Yashas","year":"2018","unstructured":"Yashas Rai, Patrick Le\u00a0Callet, and Philippe Guillotel. 2018. Salient360: Head and eye movement dataset for omnidirectional content. https:\/\/zenodo.org\/records\/10650505 Accessed: 2026-01-23."},{"key":"e_1_3_3_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/WIFS.2010.5711449"},{"key":"e_1_3_3_69_1","doi-asserted-by":"crossref","unstructured":"Adi Shamir. 1979. How to share a secret. Commun. ACM 22 11 (1979) 612\u2013613.","DOI":"10.1145\/359168.359176"},{"key":"e_1_3_3_70_1","first-page":"77","volume-title":"International Conference on Document Analysis and Recognition","author":"S\u00f6nnichsen Malte","year":"2025","unstructured":"Malte S\u00f6nnichsen, Mayar Elfares, Yao Wang, Ralf K\u00fcsters, Alina Roitberg, and Andreas Bulling. 2025. AttentionLeak: What Does Human Attention Reveal About Information Visualisation?. In International Conference on Document Analysis and Recognition. Springer, 77\u201395."},{"key":"e_1_3_3_71_1","doi-asserted-by":"publisher","DOI":"10.1145\/3649902.3653335"},{"key":"e_1_3_3_72_1","doi-asserted-by":"publisher","DOI":"10.1145\/3314111.3319915"},{"key":"e_1_3_3_73_1","doi-asserted-by":"publisher","DOI":"10.1145\/3314111.3319915"},{"key":"e_1_3_3_74_1","doi-asserted-by":"crossref","unstructured":"Adina\u00a0S Wagner Yaroslav\u00a0O Halchenko and Michael Hanke. 2019. multimatch-gaze: The MultiMatch algorithm for gaze path comparison in Python. Journal of Open Source Software 4 40 (2019) 1525.","DOI":"10.21105\/joss.01525"},{"key":"e_1_3_3_75_1","doi-asserted-by":"publisher","unstructured":"Frederike Wenzlaff Peer Briken and Arne Dekker. 2016. Video-Based Eye Tracking in Sex Research: A Systematic Literature Review. The Journal of Sex Research 53 8 (2016) 1008\u20131019. doi:10.1080\/00224499.2015.1107524","DOI":"10.1080\/00224499.2015.1107524"},{"key":"e_1_3_3_76_1","doi-asserted-by":"publisher","DOI":"10.4230\/LIPIcs.ESA.2022.90"},{"key":"e_1_3_3_77_1","doi-asserted-by":"publisher","DOI":"10.1109\/SFCS.1982.38"},{"key":"e_1_3_3_78_1","doi-asserted-by":"publisher","DOI":"10.1109\/SFCS.1986.25"}],"container-title":["Proceedings of the ACM on Human-Computer Interaction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3806022","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T13:00:42Z","timestamp":1779973242000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3806022"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,28]]},"references-count":77,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,5,31]]}},"alternative-id":["10.1145\/3806022"],"URL":"https:\/\/doi.org\/10.1145\/3806022","relation":{},"ISSN":["2573-0142"],"issn-type":[{"value":"2573-0142","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,28]]},"assertion":[{"value":"2026-05-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}