{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T16:43:47Z","timestamp":1763570627770,"version":"3.45.0"},"reference-count":47,"publisher":"Association for Computing Machinery (ACM)","issue":"4","funder":[{"DOI":"10.13039\/501100000943","name":"Commonwealth Scientific and Industrial Research Organisation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000943","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001773","name":"University of New South Wales","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001773","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Priv. Secur."],"published-print":{"date-parts":[[2025,11,30]]},"abstract":"<jats:p>\n                    Deceptive files, often called honeyfiles, have become an established tool in cyber security. Advances in machine learning (ML) models for content generation now allow the synthesis of deceptive material automatically and at scale. Metrics to quantify honeyfile attributes are thus essential to creating and evaluating effective deceptions. The two critical aspects of honeyfiles for which metrics are useful are\n                    <jats:italic toggle=\"yes\">enticement<\/jats:italic>\n                    and\n                    <jats:italic toggle=\"yes\">realism<\/jats:italic>\n                    . Enticement is the ability to attract the attention of intruders or users with malicious intent. Realism measures the similarity of deceptive artefacts to the objects they mimic.\n                  <\/jats:p>\n                  <jats:p>\n                    In the honeyfile literature, metrics for these attributes have been proposed: the Common Token Count (CTC)\u00a0[\n                    <jats:xref ref-type=\"bibr\">1<\/jats:xref>\n                    ], and Topic Semantic Matching (TSM)\u00a0[\n                    <jats:xref ref-type=\"bibr\">2<\/jats:xref>\n                    ] scores for enticement, and coherence and cohesion\u00a0[\n                    <jats:xref ref-type=\"bibr\">3<\/jats:xref>\n                    ] for realism. In this study, we compare these metrics to the perceptions of human users exposed to text samples in a simulated data breach scenario on a crowd-sourcing platform. We recruited participants to judge the realism and enticement of honeyfile text generated using several techniques. The main findings are: (i) for the enticement metrics, TSM is aligned with the perceived enticement (p-value&lt;0.001), while for the CTC score, we find inconsistent and inconclusive results, and (ii) for the realism metrics, cohesion and coherence, do not consistently align with perceived realism.\n                  <\/jats:p>","DOI":"10.1145\/3763792","type":"journal-article","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T11:36:52Z","timestamp":1758195412000},"page":"1-34","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating Honeyfile Realism and Enticement Metrics"],"prefix":"10.1145","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4621-3363","authenticated-orcid":false,"given":"Roelien","family":"Timmer","sequence":"first","affiliation":[{"name":"University of New South Wales","place":["Sydney, Australia"]},{"name":"CSIRO Data61","place":["Sydney, Australia"]},{"name":"Cyber Security CRC","place":["Sydney, Australia"]},{"name":"University of New South Wales","place":["Sydney, Australia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0561-7931","authenticated-orcid":false,"given":"David","family":"Liebowitz","sequence":"additional","affiliation":[{"name":"University of New South Wales","place":["Sydney, Australia"]},{"name":"Penten","place":["Sydney, Australia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3289-6599","authenticated-orcid":false,"given":"Surya","family":"Nepal","sequence":"additional","affiliation":[{"name":"CSIRO Data61","place":["Sydney, Australia"]},{"name":"Cyber Security CRC","place":["Sydney, Australia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1835-3475","authenticated-orcid":false,"given":"Salil","family":"Kanhere","sequence":"additional","affiliation":[{"name":"University of New South Wales","place":["Sydney, Australia"]},{"name":"Cyber Security CRC","place":["Sydney, Australia"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,11,19]]},"reference":[{"key":"e_1_3_3_2_2","volume-title":"Proceedings of the 50th Hawaii International Conference System Sciences","author":"Whitham Ben","year":"2017","unstructured":"Ben Whitham. 2017. Automating the generation of enticing text content for high-interaction honeyfiles. In Proceedings of the 50th Hawaii International Conference System Sciences."},{"key":"e_1_3_3_3_2","volume-title":"Proceedings of the 55th Hawaii International Conference System Sciences","author":"Timmer Roelien C.","year":"2022","unstructured":"Roelien C. Timmer, David Liebowitz, Surya Nepal, and Salil Kanhere. 2022. TSM: Measuring the enticement of honeyfiles with natural language processing. In Proceedings of the 55th Hawaii International Conference System Sciences."},{"key":"e_1_3_3_4_2","first-page":"31","volume-title":"Proceedings of the 1st International Workshop on Language Cognition and Computational Models","author":"Karuna Prakruthi","year":"2018","unstructured":"Prakruthi Karuna, Hemant Purohit, Ozlem Uzuner, Sushil Jajodia, and Rajesh Ganesan. 2018. Enhancing cohesion and coherence of fake text to improve believability for deceiving cyber attackers. In Proceedings of the 1st International Workshop on Language Cognition and Computational Models. 31\u201340."},{"key":"e_1_3_3_5_2","volume-title":"Honeypots: Tracking Hackers","author":"Spitzner Lance","year":"2003","unstructured":"Lance Spitzner. 2003. Honeypots: Tracking Hackers. Vol. 1. Addison-Wesley Reading."},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.5555\/67554"},{"issue":"5","key":"e_1_3_3_7_2","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1145\/42411.42412","article-title":"Stalking the wily hacker","volume":"31","author":"Stoll Clifford","year":"1988","unstructured":"Clifford Stoll. 1988. Stalking the wily hacker. Communications of the ACM 31, 5 (1988), 484\u2013497.","journal-title":"Communications of the ACM"},{"key":"e_1_3_3_8_2","first-page":"116","volume-title":"Proceedings of the 5th Annual SMC Information Assurance Workshop","author":"Yuill Jim","year":"2004","unstructured":"Jim Yuill, Mike Zappe, Dorothy Denning, and Fred Feer. 2004. Honeyfiles: Deceptive files for intrusion detection. In Proceedings of the 5th Annual SMC Information Assurance Workshop. IEEE, 116\u2013122."},{"key":"e_1_3_3_9_2","first-page":"170","volume-title":"Proceedings of the 2nd International Conference Cyber Security, Cyber Peacefare and Digital Forensic (CyberSec2013)","author":"Whitham Ben","year":"2013","unstructured":"Ben Whitham. 2013. Canary files: Generating fake files to detect critical data loss from complex computer networks. In Proceedings of the 2nd International Conference Cyber Security, Cyber Peacefare and Digital Forensic (CyberSec2013). The Society of Digital Information and Wireless Communication, 170\u2013179."},{"key":"e_1_3_3_10_2","first-page":"51","volume-title":"Proceedings of the SecureComm","volume":"19","author":"Bowen Brian M","year":"2009","unstructured":"Brian M Bowen, Shlomo Hershkop, Angelos D Keromytis, and Salvatore J Stolfo. 2009. Baiting inside attackers using decoy documents.. In Proceedings of the SecureComm, Vol. 19. Springer, 51\u201370."},{"key":"e_1_3_3_11_2","first-page":"3","volume-title":"Proceedings of the 8th European Workshop on System Security","author":"Voris Jonathan","year":"2015","unstructured":"Jonathan Voris, Jill Jermyn, Nathaniel Boggs, and Salvatore Stolfo. 2015. Fox in the trap: Thwarting masqueraders via automated decoy document deployment. In Proceedings of the 8th European Workshop on System Security. ACM, 3."},{"issue":"5","key":"e_1_3_3_12_2","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/MSPEC.2003.1197473","article-title":"Digital decoys [fake MP3 song files to deter music pirating]","volume":"40","author":"Kushner David","year":"2003","unstructured":"David Kushner. 2003. Digital decoys [fake MP3 song files to deter music pirating]. IEEE Spectrum 40, 5 (2003), 27\u201327.","journal-title":"IEEE Spectrum"},{"key":"e_1_3_3_13_2","unstructured":"Ross Gruetzemacher. 2022. The Power of Natural Language Processing. (2022). Retrieved from https:\/\/hbr.org\/search?term=ross%20gruetzemacher"},{"issue":"8","key":"e_1_3_3_14_2","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford Alec","year":"2019","unstructured":"Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI Blog 1, 8 (2019), 9.","journal-title":"OpenAI Blog"},{"key":"e_1_3_3_15_2","first-page":"8821","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Ramesh Aditya","year":"2021","unstructured":"Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021. Zero-shot text-to-image generation. In Proceedings of the International Conference on Machine Learning. PMLR, 8821\u20138831."},{"key":"e_1_3_3_16_2","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown Tom","year":"2020","unstructured":"Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language models are few-shot learners. Advances in Neural Information Processing Systems 33 (2020), 1877\u20131901.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSystem2020.2980177"},{"issue":"5","key":"e_1_3_3_18_2","first-page":"1891","article-title":"EDGE: An enticing deceptive-content generator as defensive deception","volume":"15","author":"Li Huanruo","year":"2021","unstructured":"Huanruo Li, Yunfei Guo, Shumin Huo, and Yuehang Ding. 2021. EDGE: An enticing deceptive-content generator as defensive deception. KSII Transactions on Internet and Information Systems 15, 5 (2021), 1891\u20131908.","journal-title":"KSII Transactions on Internet and Information Systems"},{"key":"e_1_3_3_19_2","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1007\/978-3-030-86137-7_3","volume-title":"Proceedings of the International Conference on Wireless Algorithms, Systems, and Applications","author":"Feng Yun","year":"2021","unstructured":"Yun Feng, Baoxu Liu, Yue Zhang, Jinli Zhang, Chaoge Liu, and Qixu Liu. 2021. Automated honey document generation using genetic algorithm. In Proceedings of the International Conference on Wireless Algorithms, Systems, and Applications. Springer, 20\u201328."},{"key":"e_1_3_3_20_2","first-page":"6945","volume-title":"Proceedings of the 54th Hawaii International Conference System Sciences","author":"Nguyen David","year":"2021","unstructured":"David Nguyen, David Liebowitz, Surya Nepal, and Salil Kanhere. 2021. HoneyCode: Automating deceptive software repositories with deep generative models. In Proceedings of the 54th Hawaii International Conference System Sciences. 6945."},{"volume-title":"Proceedings of the 7th IEEE European Symposium on Security and Privacy, (EuroS&P) 2022","author":"Moore Kristen","key":"e_1_3_3_21_2","unstructured":"Kristen Moore, Cody James Christopher, David Liebowitz, Surya Nepal, and Renee Selvey. Modelling direct messaging networks with multiple recipients for cyber deception. In Proceedings of the 7th IEEE European Symposium on Security and Privacy, (EuroS&P) 2022."},{"issue":"2","key":"e_1_3_3_22_2","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1109\/TDSC.2019.2898661","article-title":"A fake online repository generation engine for cyber deception","volume":"18","author":"Chakraborty Tanmoy","year":"2019","unstructured":"Tanmoy Chakraborty, Sushil Jajodia, Jonathan Katz, Antonio Picariello, Giancarlo Sperli, and VS Subrahmanian. 2019. A fake online repository generation engine for cyber deception. IEEE Transactions on Dependable and Secure Computing 18, 2 (2019), 518\u2013533.","journal-title":"IEEE Transactions on Dependable and Secure Computing"},{"key":"e_1_3_3_23_2","article-title":"Generating realistic fake equations in order to reduce intellectual property theft","author":"Xiong Yanhai","year":"2020","unstructured":"Yanhai Xiong, Giridhar Kaushik Ramachandran, Rajesh Ganesan, Sushil Jajodia, and VS Subrahmanian. 2020. Generating realistic fake equations in order to reduce intellectual property theft. IEEE Transactions on Dependable and Secure Computing (2020).","journal-title":"IEEE Transactions on Dependable and Secure Computing"},{"key":"e_1_3_3_24_2","article-title":"A survey of defensive deception: Approaches using game theory and machine learning","author":"Zhu Mu","year":"2021","unstructured":"Mu Zhu, Ahmed H Anwar, Zelin Wan, Jin-Hee Cho, Charles Kamhoua, and Munindar P Singh. 2021. A survey of defensive deception: Approaches using game theory and machine learning. IEEE Communications Surveys & Tutorials (2021).","journal-title":"IEEE Communications Surveys & Tutorials"},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3214305"},{"issue":"1","key":"e_1_3_3_26_2","first-page":"103","article-title":"Automating the generation of fake documents to detect network intruders","volume":"2","author":"Whitham Ben","year":"2013","unstructured":"Ben Whitham. 2013. Automating the generation of fake documents to detect network intruders. International Journal of Cyber-Security and Digital Forensics (IJCSDF) 2, 1 (2013), 103\u2013118.","journal-title":"International Journal of Cyber-Security and Digital Forensics (IJCSDF)"},{"key":"e_1_3_3_27_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-3019"},{"key":"e_1_3_3_28_2","unstructured":"Anton Bakhtin Sam Gross Myle Ott Yuntian Deng Marc\u2019Aurelio Ranzato and Arthur Szlam. 2019. Real or fake? Learning to discriminate machine from human generated text. arXiv:1906.03351. Retrieved from https:\/\/arxiv.org\/abs\/1906.03351. (2019)."},{"key":"e_1_3_3_29_2","first-page":"7282","volume-title":"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)","author":"Clark Elizabeth","year":"2021","unstructured":"Elizabeth Clark, Tal August, Sofia Serrano, Nikita Haduong, Suchin Gururangan, and Noah A Smith. 2021. All that\u2019s humanis not gold: Evaluating human evaluation of generated text. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 7282\u20137296."},{"key":"e_1_3_3_30_2","doi-asserted-by":"crossref","first-page":"1808","DOI":"10.18653\/v1\/2020.acl-main.164","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","author":"Ippolito Daphne","year":"2020","unstructured":"Daphne Ippolito, Daniel Duckworth, Chris Callison-Burch, and Douglas Eck. 2020. Automatic detection of generated text is easiest when humans are fooled. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 1808\u20131822."},{"key":"e_1_3_3_31_2","first-page":"1085","volume-title":"Proceedings of the IJCAI","volume":"5","author":"Lapata Mirella","year":"2005","unstructured":"Mirella Lapata and Regina Barzilay. 2005. Automatic evaluation of text coherence: Models and representations. In Proceedings of the IJCAI, Vol. 5. 1085\u20131090."},{"issue":"4","key":"e_1_3_3_32_2","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.3758\/s13428-015-0651-7","article-title":"The tool for the automatic analysis of text cohesion (TAACO): Automatic assessment of local, global, and text cohesion","volume":"48","author":"Crossley Scott A","year":"2016","unstructured":"Scott A Crossley, Kristopher Kyle, and Danielle S McNamara. 2016. The tool for the automatic analysis of text cohesion (TAACO): Automatic assessment of local, global, and text cohesion. Behavior Research Methods 48, 4 (2016), 1227\u20131237.","journal-title":"Behavior Research Methods"},{"key":"e_1_3_3_33_2","volume-title":"Honeypots: A New Paradigm to Information Security","author":"Joshi RC","year":"2011","unstructured":"RC Joshi and Anjali Sardana. 2011. Honeypots: A New Paradigm to Information Security. CRC Press."},{"key":"e_1_3_3_34_2","first-page":"35","volume-title":"Proceedings of the International Conference Detection of Intrusions and Malware, and Vulnerability Assessment","author":"Salem Malek Ben","year":"2011","unstructured":"Malek Ben Salem and Salvatore J Stolfo. 2011. Decoy document deployment for effective masquerade attack detection. In Proceedings of the International Conference Detection of Intrusions and Malware, and Vulnerability Assessment. Springer, 35\u201354."},{"key":"e_1_3_3_35_2","first-page":"386","volume-title":"Proceedings of the Parallel, Distributed and Network-based Processing 25th Euromicro International Conference","author":"Rauti Sampsa","year":"2017","unstructured":"Sampsa Rauti and Ville Lepp\u00e4nen. 2017. A survey on fake entities as a method to detect and monitor malicious activity. In Proceedings of the Parallel, Distributed and Network-based Processing 25th Euromicro International Conference. IEEE, 386\u2013390."},{"key":"e_1_3_3_36_2","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1145\/1741866.1741880","volume-title":"Proceedings of the 3rd ACM Conference Wireless Network Security","author":"Bowen Brian M","year":"2010","unstructured":"Brian M Bowen, Vasileios P Kemerlis, Pratap Prabhu, Angelos D Keromytis, and Salvatore J Stolfo. 2010. Automating the injection of believable decoys to detect snooping. In Proceedings of the 3rd ACM Conference Wireless Network Security. ACM, 81\u201386."},{"key":"e_1_3_3_37_2","volume-title":"Proceedings of the 30th USENIX Security Symposium (USENIX Security 21)","author":"Ferguson-Walter Kimberly J","year":"2021","unstructured":"Kimberly J Ferguson-Walter, Maxine M Major, Chelsea K Johnson, and Daniel H Muhleman. 2021. Examining the efficacy of decoy-based and psychological cyber deception. In Proceedings of the 30th USENIX Security Symposium (USENIX Security 21)."},{"issue":"1","key":"e_1_3_3_38_2","first-page":"11","article-title":"Why people use lorem ipsum to represent dummy text? The research of lorem ipsum","volume":"1","author":"Team Open Journal Theme","year":"2019","unstructured":"Open Journal Theme Team. 2019. Why people use lorem ipsum to represent dummy text? The research of lorem ipsum. Journal Of Education 1, 1 (2019), 11\u201316.","journal-title":"Journal Of Education"},{"key":"e_1_3_3_39_2","unstructured":"U.S. Bureau of Labor Statistics. 2021. Employment Projections Data Definitions. (2021). Retrieved from https:\/\/www.bls.gov\/emp\/documentation\/definitions.htm#education"},{"key":"e_1_3_3_40_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1980.tb01109.x"},{"issue":"5","key":"e_1_3_3_41_2","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1016\/0167-9473(96)00004-7","article-title":"Random effects in ordinal regression models","volume":"22","author":"Tutz Gerhard","year":"1996","unstructured":"Gerhard Tutz and Wolfgang Hennevogl. 1996. Random effects in ordinal regression models. Computational Statistics & Data Analysis 22, 5 (1996), 537\u2013557.","journal-title":"Computational Statistics & Data Analysis"},{"key":"e_1_3_3_42_2","article-title":"Cumulative link models for ordinal regression with the R package ordinal","volume":"35","author":"Christensen Rune Haubo B","year":"2018","unstructured":"Rune Haubo B Christensen. 2018. Cumulative link models for ordinal regression with the R package ordinal. Submitted in Journal of Statistical Software 35 (2018).","journal-title":"Submitted in Journal of Statistical Software"},{"issue":"4","key":"e_1_3_3_43_2","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1111\/j.1469-1809.1972.tb00293.x","article-title":"The log likelihood ratio test (the G-test)","volume":"21","author":"Woolf Barnet","year":"1957","unstructured":"Barnet Woolf. 1957. The log likelihood ratio test (the G-test). Annals of Human Genetics 21, 4 (1957), 397\u2013409.","journal-title":"Annals of Human Genetics"},{"key":"e_1_3_3_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3578503.3583622"},{"key":"e_1_3_3_45_2","doi-asserted-by":"publisher","DOI":"10.1177\/001316446002000104"},{"key":"e_1_3_3_46_2","doi-asserted-by":"publisher","DOI":"10.1093\/pan\/mpr057"},{"key":"e_1_3_3_47_2","first-page":"993","article-title":"Latent dirichlet allocation","volume":"3","author":"Blei David M","year":"2003","unstructured":"David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. Journal of Machine Learning Research 3, Jan (2003), 993\u20131022.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_3_48_2","article-title":"Large language models will define artificial intelligence","author":"Drenik Gary","unstructured":"Gary Drenik. Large language models will define artificial intelligence. Forber ([n. d.]). Retrieved from https:\/\/www.forbes.com\/sites\/garydrenik\/2023\/01\/11\/large-language-models-will-define-artificial-intelligence\/?sh=169be6dbb60f","journal-title":"Forber"}],"container-title":["ACM Transactions on Privacy and Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3763792","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T16:40:20Z","timestamp":1763570420000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3763792"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,19]]},"references-count":47,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,11,30]]}},"alternative-id":["10.1145\/3763792"],"URL":"https:\/\/doi.org\/10.1145\/3763792","relation":{},"ISSN":["2471-2566","2471-2574"],"issn-type":[{"type":"print","value":"2471-2566"},{"type":"electronic","value":"2471-2574"}],"subject":[],"published":{"date-parts":[[2025,11,19]]},"assertion":[{"value":"2023-04-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-29","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-19","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}