{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T09:48:55Z","timestamp":1787910535335,"version":"build-2784847793"},"publisher-location":"New York, NY, USA","reference-count":47,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T00:00:00Z","timestamp":1740960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,3,3]]},"DOI":"10.1145\/3706468.3706493","type":"proceedings-article","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T09:04:11Z","timestamp":1740128651000},"page":"181-191","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["Advancing privacy in learning analytics using differential privacy"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4973-0901","authenticated-orcid":false,"given":"Qinyi","family":"Liu","sequence":"first","affiliation":[{"name":"Centre for the Science of Learning &amp; Technology (SLATE), University of Bergen, Bergen, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8106-2198","authenticated-orcid":false,"given":"Ronas","family":"Shakya","sequence":"additional","affiliation":[{"name":"Centre for the Science of Learning &amp; Technology (SLATE), University of Bergen, Bergen, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6860-4404","authenticated-orcid":false,"given":"Mohammad","family":"Khalil","sequence":"additional","affiliation":[{"name":"Centre for the Science of Learning &amp; Technology (SLATE), University of Bergen, Bergen, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1904-0446","authenticated-orcid":false,"given":"Jelena","family":"Jovanovic","sequence":"additional","affiliation":[{"name":"University of Belgrade, Centre for the Science of Learning &amp; Technology (SLATE), University of Bergen, Bergen, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,3,3]]},"reference":[{"key":"e_1_3_3_2_2_2","unstructured":"Amazon. 2024. How differential privacy helps unlock insights without revealing data at the individual-level | Amazon Web Services. https:\/\/aws.amazon.com\/blogs\/industries\/how-differential-privacy-helps-unlock-insights-without-revealing-data-at-the-individual-level\/"},{"key":"e_1_3_3_2_3_2","doi-asserted-by":"publisher","unstructured":"Arno Appenzeller Moritz Leitner Patrick Philipp Erik Krempel and J\u00fcrgen Beyerer. 2022. Privacy and Utility of Private Synthetic Data for Medical Data Analyses. Applied Sciences 12 (12 2022) 12320. 10.3390\/app122312320","DOI":"10.3390\/app122312320"},{"key":"e_1_3_3_2_4_2","unstructured":"Kamalika Chaudhuri Claire Monteleoni and Anand Sarwate. 2011. Differentially Private Empirical Risk Minimization. Journal of Machine Learning Research 12 (2011) 1069\u20131109. https:\/\/www.jmlr.org\/papers\/volume12\/chaudhuri11a\/chaudhuri11a.pdf"},{"key":"e_1_3_3_2_5_2","unstructured":"Sam Cook. 2024. US schools leaked 24.5 million records in 1 327 data breaches since 2005. https:\/\/www.comparitech.com\/blog\/vpn-privacy\/us-schools-data-breaches\/"},{"key":"e_1_3_3_2_6_2","volume-title":"Hands-On Differential Privacy","author":"Cowan Ethan","year":"2024","unstructured":"Ethan Cowan, Michael Shoemate, and Mayana Pereira. 2024. Hands-On Differential Privacy. O\u2019Reilly Media, Inc.https:\/\/www.oreilly.com\/library\/view\/hands-on-differential-privacy\/9781492097730\/"},{"key":"e_1_3_3_2_7_2","doi-asserted-by":"publisher","unstructured":"Rachel Cummings Damien Desfontaines David Evans Roxana Geambasu Yangsibo Huang Matthew Jagielski Peter Kairouz Gautam Kamath Sewoong Oh Olga Ohrimenko Nicolas Papernot Ryan Rogers Milan Shen Shuang Song Weijie Su Andreas Terzis Abhradeep Thakurta Sergei Vassilvitskii Yu-Xiang Wang Li Xiong Sergey Yekhanin Da Yu Huanyu Zhang and Wanrong Zhang. 2024. Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment. Harvard Data Science Review 6 (01 2024). 10.1162\/99608f92.d3197524","DOI":"10.1162\/99608f92.d3197524"},{"key":"e_1_3_3_2_8_2","doi-asserted-by":"publisher","unstructured":"Rachel Cummings Gabriel Kaptchuk and Elissa\u00a0M Redmiles. 2021. \"I need a better description\": An Investigation Into User Expectations For Differential Privacy. Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security (CCS \u201921) (2021). 10.1145\/3460120.3485252","DOI":"10.1145\/3460120.3485252"},{"key":"e_1_3_3_2_9_2","unstructured":"Oblivious Devs. 2023. Understanding Differential Privacy: A Non-Technical Perspective. https:\/\/medium.com\/@oblv\/the-role-of-differential-privacy-in-reshaping-the-data-world-83271ea67ee0"},{"key":"e_1_3_3_2_10_2","unstructured":"Victoria Drake. 2022. Threat Modeling | OWASP. https:\/\/owasp.org\/www-community\/Threat_Modeling"},{"key":"e_1_3_3_2_11_2","doi-asserted-by":"publisher","unstructured":"Cynthia Dwork. 2006. Differential Privacy. Automata Languages and Programming 4052 (2006) 1\u201312. 10.1007\/11787006_1","DOI":"10.1007\/11787006_1"},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"publisher","unstructured":"Cynthia Dwork Frank McSherry Kobbi Nissim and Adam Smith. 2006. Calibrating Noise to Sensitivity in Private Data Analysis. Theory of Cryptography (2006) 265\u2013284. 10.1007\/11681878_14","DOI":"10.1007\/11681878_14"},{"key":"e_1_3_3_2_13_2","doi-asserted-by":"crossref","unstructured":"Kazuto Fukuchi Quang\u00a0Khai Tran and Jun Sakuma. 2017. Differentially Private Empirical Risk Minimization with Input Perturbation. arXiv.org (2017). https:\/\/arxiv.org\/abs\/1710.07425","DOI":"10.1007\/978-3-319-67786-6_6"},{"key":"e_1_3_3_2_14_2","doi-asserted-by":"publisher","unstructured":"Mehmet\u00a0Emre Gursoy Ali Inan Mehmet\u00a0Ercan Nergiz and Yucel Saygin. 2017. Privacy-Preserving Learning Analytics: Challenges and Techniques. IEEE Transactions on Learning Technologies 10 (01 2017) 68\u201381. 10.1109\/tlt.2016.2607747","DOI":"10.1109\/tlt.2016.2607747"},{"key":"e_1_3_3_2_15_2","doi-asserted-by":"publisher","unstructured":"Naoise Holohan Stefano Braghin P\u00f3l Mac\u00a0Aonghusa and Killian Levacher. 2019. Diffprivlib: The IBM Differential Privacy Library. arXiv (2019). 10.48550\/arXiv.1907.02444","DOI":"10.48550\/arXiv.1907.02444"},{"key":"e_1_3_3_2_16_2","doi-asserted-by":"publisher","unstructured":"Stephen Hutt Ryan\u00a0S. Baker Michael\u00a0Mogessie Ashenafi Juan\u00a0Miguel Andres\u2010Bray and Christopher Brooks. 2022. Controlled outputs full data: A privacy\u2010protecting infrastructure for <scp>MOOC<\/scp> data. British Journal of Educational Technology 53 (05 2022) 756\u2013775. 10.1111\/bjet.13231","DOI":"10.1111\/bjet.13231"},{"key":"e_1_3_3_2_17_2","doi-asserted-by":"publisher","unstructured":"Malinka Ivanova Iskra Trifonova and Galina Bogdanova. 2022. Privacy Preservation in eLearning: Exploration and Analysis. 20th International Conference on Information Technology Based Higher Education and Training (ITHET) (2022). 10.1109\/ithet56107.2022.10031904","DOI":"10.1109\/ithet56107.2022.10031904"},{"key":"e_1_3_3_2_18_2","doi-asserted-by":"publisher","unstructured":"Priyank Jain Manasi Gyanchandani and Nilay Khare. 2018. Differential privacy: its technological prescriptive using big data. Journal of Big Data 5 (04 2018). 10.1186\/s40537-018-0124-9","DOI":"10.1186\/s40537-018-0124-9"},{"key":"e_1_3_3_2_19_2","unstructured":"Ismat Jarin and Birhanu Eshete. 2021. DP-UTIL: Comprehensive Utility Analysis of Differential Privacy in Machine Learning. arXiv.org (2021). https:\/\/arxiv.org\/pdf\/2112.12998"},{"key":"e_1_3_3_2_20_2","first-page":"1895","volume-title":"28th USENIX Security Symposium (USENIX Security 19)","author":"Jayaraman Bargav","year":"2019","unstructured":"Bargav Jayaraman and David Evans. 2019. Evaluating Differentially Private Machine Learning in Practice. In 28th USENIX Security Symposium (USENIX Security 19). USENIX Association, Santa Clara, CA, 1895\u20131912. https:\/\/www.usenix.org\/conference\/usenixsecurity19\/presentation\/jayaraman"},{"key":"e_1_3_3_2_21_2","unstructured":"James Jordon Lukasz Szpruch Florimond Houssiau Mirko Bottarelli Giovanni Cherubin Carsten Maple Samuel Cohen and Adrian Weller. 2022. Synthetic Data -what why and how?https:\/\/royalsociety.org\/-\/media\/policy\/projects\/privacy-enhancing-technologies\/Synthetic_Data_Survey-24.pdf"},{"key":"e_1_3_3_2_22_2","unstructured":"Georgios Kaissis Alexander Ziller Stefan Kolek Anneliese Riess and Daniel Rueckert. 2023. Optimal privacy guarantees for a relaxed threat model: Addressing sub-optimal adversaries in differentially private machine learning. 37th Conference on Neural Information Processing Systems (NeurIPS 2023). (2023). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2023\/file\/aea831d6c7af37fd4230937225be3414-Paper-Conference.pdf"},{"key":"e_1_3_3_2_23_2","unstructured":"Mark Keierleber. 2022. After Huge Illuminate Data Breach Ed Tech\u2019s \u2018Student Privacy Pledge\u2019 Under Fire. https:\/\/www.the74million.org\/article\/after-huge-illuminate-data-breach-ed-techs-student-privacy-pledge-under-fire\/"},{"key":"e_1_3_3_2_24_2","doi-asserted-by":"publisher","unstructured":"Mohammad Khalil and Martin Ebner. 2016. De-identification in learning analytics. Journal of Learning Analytics 3 (04 2016) 129\u2013138. 10.18608\/jla.2016.31.8","DOI":"10.18608\/jla.2016.31.8"},{"key":"e_1_3_3_2_25_2","doi-asserted-by":"publisher","unstructured":"Mohammad Khalil Paul Prinsloo and Sharon Slade. 2022. A Comparison of Learning Analytics Frameworks: a Systematic Review. LAK22: 12th International Learning Analytics and Knowledge Conference (03 2022). 10.1145\/3506860.3506878","DOI":"10.1145\/3506860.3506878"},{"key":"e_1_3_3_2_26_2","doi-asserted-by":"publisher","unstructured":"Mohammad Khalil Farhad Vadiee Ronas Shakya and Qinyi Liu. 2025. Creating Artificial Students that Never Existed: Leveraging Large Language Models and CTGANs for Synthetic Data Generation. In Proceedings of the 15th Learning Analytics and Knowledge Conference (LAK\u201925). ACM. (2025). 10.1145\/3706468.3706523","DOI":"10.1145\/3706468.3706523"},{"key":"e_1_3_3_2_27_2","unstructured":"Nick Kirtley. 2022. PASTA Threat Modeling - Threat-Modeling.com. https:\/\/threat-modeling.com\/pasta-threat-modeling\/"},{"key":"e_1_3_3_2_28_2","unstructured":"Alexey Kurakin and Roxana Geambasu. 2022. Applying Differential Privacy to Large Scale Image Classification. https:\/\/research.google\/blog\/applying-differential-privacy-to-large-scale-image-classification\/"},{"key":"e_1_3_3_2_29_2","doi-asserted-by":"publisher","unstructured":"Jakub Kuzilek Martin Hlosta and Zdenek Zdrahal. 2017. Open University Learning Analytics dataset. Scientific Data 4 (11 2017) 170171. 10.1038\/sdata.2017.171","DOI":"10.1038\/sdata.2017.171"},{"key":"e_1_3_3_2_30_2","doi-asserted-by":"publisher","unstructured":"Qinyi Liu Oscar Deho Farhad Vadiee Mohammad Khalil Srecko Joksimovic and George Siemens. 2025. Can Synthetic Data Be Fair and Private? A Comparative Study of Synthetic Data Generation and Fairness Algorithms. In Proceedings of the 15th International Learning Analytics and Knowledge Conference (LAK\u201925). ACM. (2025). 10.1145\/3706468.3706546","DOI":"10.1145\/3706468.3706546"},{"key":"e_1_3_3_2_31_2","doi-asserted-by":"publisher","unstructured":"Qinyi Liu and Mohammad Khalil. 2023. Understanding privacy and data protection issues in learning analytics using a systematic review. British Journal of Educational Technology 54 (09 2023). 10.1111\/bjet.13388","DOI":"10.1111\/bjet.13388"},{"key":"e_1_3_3_2_32_2","doi-asserted-by":"publisher","unstructured":"Qinyi Liu Mohammad Khalil Jelena Jovanovic and Ronas Shakya. 2024. Scaling While Privacy Preserving: A Comprehensive Synthetic Tabular Data Generation and Evaluation in Learning Analytics. Proceedings of the 14th Learning Analytics and Knowledge Conference (03 2024). 10.1145\/3636555.3636921","DOI":"10.1145\/3636555.3636921"},{"key":"e_1_3_3_2_33_2","doi-asserted-by":"publisher","unstructured":"WeiKang Liu Yanchun Zhang Hong Yang and Meng Qi. 2023. A Survey on Differential Privacy for Medical Data Analysis. Annals of Data Science (06 2023). 10.1007\/s40745-023-00475-3","DOI":"10.1007\/s40745-023-00475-3"},{"key":"e_1_3_3_2_34_2","unstructured":"Microsoft. 2022. Threats - Microsoft Threat Modeling Tool - Azure. https:\/\/learn.microsoft.com\/en-us\/azure\/security\/develop\/threat-modeling-tool-threats"},{"key":"e_1_3_3_2_35_2","doi-asserted-by":"publisher","unstructured":"Daniel\u00a0L. Oberski and Frauke Kreuter. 2020. Differential Privacy and Social Science: An Urgent Puzzle. 2.1 2 (01 2020). 10.1162\/99608f92.63a22079","DOI":"10.1162\/99608f92.63a22079"},{"key":"e_1_3_3_2_36_2","doi-asserted-by":"publisher","unstructured":"Nicolas Papernot Mart\u00edn Abadi \u00dalfar Erlingsson Ian Goodfellow and Kunal Talwar. 2017. Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data. arXiv.org (2017). 10.48550\/arXiv.1610.05755","DOI":"10.48550\/arXiv.1610.05755"},{"key":"e_1_3_3_2_37_2","unstructured":"Nicolas Papernot and Abhradeep Thakurta. 2021. How to deploy machine learning with differential privacy?https:\/\/differentialprivacy.org\/how-to-deploy-ml-with-dp\/"},{"key":"e_1_3_3_2_38_2","doi-asserted-by":"publisher","unstructured":"Ivan Podsevalov Alexei Podsevalov and Vladimir Korkhov. 2022. Differential Privacy for Statistical Data of Educational Institutions. Computational Science and Its Applications \u2013 ICCSA 2022 Workshops: Malaga Spain July 4\u20137 2022 Proceedings Part IV (01 2022) 603\u2013615. 10.1007\/978-3-031-10542-5_41","DOI":"10.1007\/978-3-031-10542-5_41"},{"key":"e_1_3_3_2_39_2","doi-asserted-by":"publisher","unstructured":"Natalia Ponomareva Hussein Hazimeh Alexey Kurakin Zhen-Liang Xu Carson Denison McMahan\u00a0H Brendan Sergei Vassilvitskii Steve Chien and Abhradeep Thakurta. 2023. How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy. Journal of Artificial Intelligence Research 77 (07 2023) 1113\u20131201. 10.1613\/jair.1.14649","DOI":"10.1613\/jair.1.14649"},{"key":"e_1_3_3_2_40_2","doi-asserted-by":"publisher","unstructured":"Paul Prinsloo and Sharon Slade. 2015. Student privacy self-management: implications for learning analytics Conference or Workshop Item Student privacy self-management: implications for learning analytics. Conference: LAK15 (5th Learning Analytics and Knowledge Conference) (2015) 83\u201392. 10.1145\/2723576.2723585","DOI":"10.1145\/2723576.2723585"},{"key":"e_1_3_3_2_41_2","doi-asserted-by":"publisher","unstructured":"Paul Prinsloo Sharon Slade and Mohammad Khalil. 2022. The answer is (not only) technological: Considering student data privacy in learning analytics. British Journal of Educational Technology 53 4 (2022) 876\u2013893. 10.1111\/bjet.13216","DOI":"10.1111\/bjet.13216"},{"key":"e_1_3_3_2_42_2","doi-asserted-by":"publisher","unstructured":"Frank Stinar Zihan Xiong and Nigel Bosch. 2024. An Approach to Improve k-Anonymization Practices in Educational Data Mining. Journal of Educational Data Mining Publishing Home 16 (2024). 10.5281\/zenodo.11056083","DOI":"10.5281\/zenodo.11056083"},{"key":"e_1_3_3_2_43_2","doi-asserted-by":"publisher","unstructured":"Agung Triayudi Iskandar Fitri Sitti Rachmawati\u00a0Yahya and Sumiati Sumiati. 2023. Educational Data Mining Patterns K-anonymity for The Analytics of Student Privacy Data. 2023 International Conference on Computer Science Information Technology and Engineering (ICCoSITE) (2023). 10.1109\/iccosite57641.2023.10127762","DOI":"10.1109\/iccosite57641.2023.10127762"},{"key":"e_1_3_3_2_44_2","doi-asserted-by":"publisher","unstructured":"Gatha Varma Ritu Chauhan and Dhananjay Singh. 2022. Sarve: synthetic data and local differential privacy for private frequency estimation. Cybersecurity 5 (08 2022). 10.1186\/s42400-022-00129-6","DOI":"10.1186\/s42400-022-00129-6"},{"key":"e_1_3_3_2_45_2","unstructured":"Bob Violino. 2022. Data privacy rules are sweeping across the globe and getting stricter. CNBC (12 2022). https:\/\/www.cnbc.com\/2022\/12\/22\/data-privacy-rules-are-sweeping-across-the-globe-and-getting-stricter.html"},{"key":"e_1_3_3_2_46_2","doi-asserted-by":"publisher","unstructured":"Elad Yacobson Orly Fuhrman Sara Hershkovitz and Giora Alexandron. 2021. De-identification is Insufficient to Protect Student Privacy or \u2013 What Can a Field Trip Reveal? Journal of Learning Analytics 8 (09 2021) 83\u201392. 10.18608\/jla.2021.7353","DOI":"10.18608\/jla.2021.7353"},{"key":"e_1_3_3_2_47_2","doi-asserted-by":"publisher","unstructured":"Xiang Yue Minxin Du Tianhao Wang Yaliang Li Huan Sun and Sherman S.\u00a0M. Chow. 2021. Differential Privacy for Text Analytics via Natural Text Sanitization. 10.48550\/arXiv.2106.01221","DOI":"10.48550\/arXiv.2106.01221"},{"key":"e_1_3_3_2_48_2","doi-asserted-by":"publisher","unstructured":"Chen Zhan Srecko Joksimovi Djazia Ladjal Thierry Rakotoarivelo Ruth Marshall and Abelardo Pardo. 2024. Preserving Both Privacy and Utility in Learning Analytics. IEEE transactions on learning technologies (01 2024) 1\u201314. 10.1109\/tlt.2024.3393766","DOI":"10.1109\/tlt.2024.3393766"}],"event":{"name":"LAK '25: The 15th International Learning Analytics and Knowledge Conference","location":"Dublin Ireland","acronym":"LAK 2025"},"container-title":["Proceedings of the 15th International Learning Analytics and Knowledge Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3706468.3706493","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3706468.3706493","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T21:56:50Z","timestamp":1750283810000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3706468.3706493"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,3]]},"references-count":47,"alternative-id":["10.1145\/3706468.3706493","10.1145\/3706468"],"URL":"https:\/\/doi.org\/10.1145\/3706468.3706493","relation":{},"subject":[],"published":{"date-parts":[[2025,3,3]]},"assertion":[{"value":"2025-03-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}