{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T19:10:42Z","timestamp":1768072242566,"version":"3.49.0"},"reference-count":64,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2021,6,23]],"date-time":"2021-06-23T00:00:00Z","timestamp":1624406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"ANR","award":["17-CE25-0017"],"award-info":[{"award-number":["17-CE25-0017"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2021,6,23]]},"abstract":"<jats:p>Crowd sensing applications have demonstrated their usefulness in many real-life scenarios (e.g., air quality monitoring, traffic and noise monitoring). Preserving the privacy of crowd sensing app users is becoming increasingly important as the collected geo-located data may reveal sensitive information about these users (e.g., home, work places, political, religious, sexual preferences). In this context, a large variety of Location Privacy Protection Mechanisms (LPPMs) have been proposed. However, each LPPM comes with a given set of configuration parameters. The value of these parameters impacts not only the privacy level but also the utility of the resulting data. Choosing the right LPPM and the right configuration for reaching a satisfactory privacy vs. utility tradeoff is generally a difficult problem mobile app developers have to face. Solving this problem is commonly done by relying on a trusted proxy server to which raw geo-located traces are sent and privacy vs. utility assessment is performed enabling the selection of the best LPPM for each trace. In this paper we present EDEN, the first solution that selects automatically the best LPPM and its corresponding configuration without sending raw geo-located traces outside the user's device. We reach this objective by relying on a federated learning approach. The evaluation of EDEN on five real-world mobility datasets shows that EDEN outperforms state-of-the-art LPPMs reaching a better privacy vs. utility tradeoff.<\/jats:p>","DOI":"10.1145\/3463502","type":"journal-article","created":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T16:29:19Z","timestamp":1624552159000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["EDEN"],"prefix":"10.1145","volume":"5","author":[{"given":"Besma","family":"Khalfoun","sequence":"first","affiliation":[{"name":"Universite de Lyon, CNRS. INSA Lyon, LIRIS, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sonia","family":"Ben Mokhtar","sequence":"additional","affiliation":[{"name":"Universite de Lyon, CNRS. INSA Lyon, LIRIS, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sara","family":"Bouchenak","sequence":"additional","affiliation":[{"name":"Universite de Lyon, CNRS. INSA Lyon, LIRIS, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vlad","family":"Nitu","sequence":"additional","affiliation":[{"name":"Universite de Lyon, CNRS. INSA Lyon, LIRIS, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,6,24]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2008.4497446"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2010.05.003"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2508859.2516735"},{"key":"e_1_2_1_4_1","first-page":"2938","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Bagdasaryan Eugene","year":"2020","unstructured":"Eugene Bagdasaryan , Andreas Veit , Yiqing Hua , Deborah Estrin , and Vitaly Shmatikov . How to backdoor federated learning . In International Conference on Artificial Intelligence and Statistics , pages 2938 -- 2948 . PMLR, 2020 . Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov. How to backdoor federated learning. In International Conference on Artificial Intelligence and Statistics, pages 2938--2948. PMLR, 2020."},{"key":"e_1_2_1_5_1","volume-title":"https:\/\/www.waze.com","author":"Bardin Noam","year":"2008","unstructured":"Noam Bardin . https:\/\/www.waze.com , 2008 . Noam Bardin. https:\/\/www.waze.com, 2008."},{"key":"e_1_2_1_7_1","first-page":"634","volume-title":"International Conference on Machine Learning","author":"Bhagoji Arjun Nitin","year":"2019","unstructured":"Arjun Nitin Bhagoji , Supriyo Chakraborty , Prateek Mittal , and Seraphin Calo . Analyzing federated learning through an adversarial lens . In International Conference on Machine Learning , pages 634 -- 643 . PMLR, 2019 . Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo. Analyzing federated learning through an adversarial lens. In International Conference on Machine Learning, pages 634--643. PMLR, 2019."},{"key":"e_1_2_1_8_1","volume-title":"Towards federated learning at scale: System design. arXiv preprint arXiv:1902.01046","author":"Bonawitz Keith","year":"2019","unstructured":"Keith Bonawitz , Hubert Eichner , Wolfgang Grieskamp , Dzmitry Huba , Alex Ingerman , Vladimir Ivanov , Chloe Kiddon , Jakub Kone\u010dny , Stefano Mazzocchi , H Brendan McMahan , Towards federated learning at scale: System design. arXiv preprint arXiv:1902.01046 , 2019 . Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Kone\u010dny, Stefano Mazzocchi, H Brendan McMahan, et al. Towards federated learning at scale: System design. arXiv preprint arXiv:1902.01046, 2019."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2971648.2971741"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1088\/0952-4746\/36\/2\/S82"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2019.2914030"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/SRDS.2017.25"},{"issue":"2","key":"e_1_2_1_15_1","first-page":"1","article-title":"Comprehensive survey on distance\/similarity measures between probability density functions","volume":"1","author":"Cha Sung-Hyuk","year":"2007","unstructured":"Sung-Hyuk Cha . Comprehensive survey on distance\/similarity measures between probability density functions . City , 1 ( 2 ): 1 , 2007 . Sung-Hyuk Cha. Comprehensive survey on distance\/similarity measures between probability density functions. City, 1(2):1, 2007.","journal-title":"City"},{"key":"e_1_2_1_16_1","volume-title":"Federated learning of out-of-vocabulary words. arXiv preprint arXiv:1903.10635","author":"Chen Mingqing","year":"2019","unstructured":"Mingqing Chen , Rajiv Mathews , Tom Ouyang , and Fran\u00e7oise Beaufays . Federated learning of out-of-vocabulary words. arXiv preprint arXiv:1903.10635 , 2019 . Mingqing Chen, Rajiv Mathews, Tom Ouyang, and Fran\u00e7oise Beaufays. Federated learning of out-of-vocabulary words. arXiv preprint arXiv:1903.10635, 2019."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3423211.3425685"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/srep01376"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2505821.2505834"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.5555\/1791834.1791836"},{"key":"e_1_2_1_21_1","volume-title":"Redditor cracks anonymous data trove to pinpoint muslim cab drivers. Online at: http:\/\/mashable.com\/2015\/01\/28\/redditor-muslim-cab-drivers","author":"Franceschi-Bicchierai Lorenzo","year":"2015","unstructured":"Lorenzo Franceschi-Bicchierai . Redditor cracks anonymous data trove to pinpoint muslim cab drivers. Online at: http:\/\/mashable.com\/2015\/01\/28\/redditor-muslim-cab-drivers , 2015 . Lorenzo Franceschi-Bicchierai. Redditor cracks anonymous data trove to pinpoint muslim cab drivers. Online at: http:\/\/mashable.com\/2015\/01\/28\/redditor-muslim-cab-drivers, 2015."},{"key":"e_1_2_1_22_1","volume-title":"Mitigating sybils in federated learning poisoning. CoRR, abs\/1808.04866","author":"Fung Clement","year":"2018","unstructured":"Clement Fung , Chris J. M. Yoon , and Ivan Beschastnikh . Mitigating sybils in federated learning poisoning. CoRR, abs\/1808.04866 , 2018 . Clement Fung, Chris J. M. Yoon, and Ivan Beschastnikh. Mitigating sybils in federated learning poisoning. CoRR, abs\/1808.04866, 2018."},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcss.2014.04.024"},{"key":"e_1_2_1_24_1","volume-title":"Mobile crowdsensing: current state and future challenges","author":"Ganti Raghu K","year":"2011","unstructured":"Raghu K Ganti , Fan Ye , and Hui Lei . Mobile crowdsensing: current state and future challenges . IEEE communications Magazine , 49(11):32--39, 2011 . Raghu K Ganti, Fan Ye, and Hui Lei. Mobile crowdsensing: current state and future challenges. IEEE communications Magazine, 49(11):32--39, 2011."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2005.48"},{"key":"e_1_2_1_26_1","volume-title":"Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557","author":"Geyer Robin C","year":"2017","unstructured":"Robin C Geyer , Tassilo Klein , and Moin Nabi . Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557 , 2017 . Robin C Geyer, Tassilo Klein, and Moin Nabi. Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557, 2017."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-01516-8_26"},{"key":"e_1_2_1_28_1","volume-title":"Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604","author":"Hard Andrew","year":"2018","unstructured":"Andrew Hard , Kanishka Rao , Rajiv Mathews , Swaroop Ramaswamy , Fran\u00e7oise Beaufays , Sean Augenstein , Hubert Eichner , Chlo\u00e9 Kiddon , and Daniel Ramage . Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604 , 2018 . Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Fran\u00e7oise Beaufays, Sean Augenstein, Hubert Eichner, Chlo\u00e9 Kiddon, and Daniel Ramage. Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604, 2018."},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2017.12.002"},{"key":"e_1_2_1_30_1","volume-title":"Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977","author":"Kairouz Peter","year":"2019","unstructured":"Peter Kairouz , H Brendan McMahan , Brendan Avent , Aur\u00e9lien Bellet , Mehdi Bennis , Arjun Nitin Bhagoji , Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977 , 2019 . Peter Kairouz, H Brendan McMahan, Brendan Avent, Aur\u00e9lien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977, 2019."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3361525.3361542"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-72037-9_8"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00779-008-0212-5"},{"key":"e_1_2_1_34_1","volume-title":"The mobile data challenge: Big data for mobile computing research. Technical report","author":"Laurila Juha K","year":"2012","unstructured":"Juha K Laurila , Daniel Gatica-Perez , Imad Aad , Olivier Bornet , Trinh- Minh-Tri Do , Olivier Dousse , Julien Eberle , Markus Miettinen , The mobile data challenge: Big data for mobile computing research. Technical report , 2012 . Juha K Laurila, Daniel Gatica-Perez, Imad Aad, Olivier Bornet, Trinh-Minh-Tri Do, Olivier Dousse, Julien Eberle, Markus Miettinen, et al. The mobile data challenge: Big data for mobile computing research. Technical report, 2012."},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2016.2604383"},{"key":"e_1_2_1_36_1","volume-title":"l-diversity: Privacy beyond k-anonymity. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(1):3-es","author":"Machanavajjhala Ashwin","year":"2007","unstructured":"Ashwin Machanavajjhala , Daniel Kifer , Johannes Gehrke , and Muthuramakrishnan Venkitasubramaniam . l-diversity: Privacy beyond k-anonymity. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(1):3-es , 2007 . Ashwin Machanavajjhala, Daniel Kifer, Johannes Gehrke, and Muthuramakrishnan Venkitasubramaniam. l-diversity: Privacy beyond k-anonymity. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(1):3-es, 2007."},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-88351-7_16"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3264934"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3144457.3144494"},{"key":"e_1_2_1_40_1","first-page":"1273","volume-title":"Artificial Intelligence and Statistics","author":"McMahan Brendan","year":"2017","unstructured":"Brendan McMahan , Eider Moore , Daniel Ramage , Seth Hampson , and Blaise Aguera y Arcas . Communication-efficient learning of deep networks from decentralized data . In Artificial Intelligence and Statistics , pages 1273 -- 1282 . PMLR, 2017 . Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. In Artificial Intelligence and Statistics, pages 1273--1282. PMLR, 2017."},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2020.07.011"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.4135\/9781412983433"},{"key":"e_1_2_1_43_1","volume-title":"EuroSys","author":"Mo Fan","year":"2019","unstructured":"Fan Mo and Hamed Haddadi . Efficient and private federated learning using tee . In EuroSys , 2019 . Fan Mo and Hamed Haddadi. Efficient and private federated learning using tee. In EuroSys, 2019."},{"key":"e_1_2_1_44_1","first-page":"763","volume-title":"Proceedings of the 32nd international conference on Very large data bases","author":"Mokbel Mohamed F","year":"2006","unstructured":"Mohamed F Mokbel , Chi-Yin Chow , and Walid G Aref . The new casper: Query processing for location services without compromising privacy . In Proceedings of the 32nd international conference on Very large data bases , pages 763 -- 774 , 2006 . Mohamed F Mokbel, Chi-Yin Chow, and Walid G Aref. The new casper: Query processing for location services without compromising privacy. In Proceedings of the 32nd international conference on Very large data bases, pages 763--774, 2006."},{"key":"e_1_2_1_45_1","first-page":"2017","article-title":"Priva'mov: Analysing human mobility through multi-sensor datasets","author":"Mokhtar Sonia Ben","year":"2017","unstructured":"Sonia Ben Mokhtar , Antoine Boutet , Louafi Bouzouina , Patrick Bonnel , Olivier Brette , Lionel Brunie , Mathieu Cunche , Stephane D'Alu , Vincent Primault , Patrice Raveneau , Priva'mov: Analysing human mobility through multi-sensor datasets . In NetMob 2017 , 2017 . Sonia Ben Mokhtar, Antoine Boutet, Louafi Bouzouina, Patrick Bonnel, Olivier Brette, Lionel Brunie, Mathieu Cunche, Stephane D'Alu, Vincent Primault, Patrice Raveneau, et al. Priva'mov: Analysing human mobility through multi-sensor datasets. In NetMob 2017, 2017.","journal-title":"NetMob"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2015.2498131"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/72.159058"},{"key":"e_1_2_1_48_1","volume-title":"Scalable private learning with pate. arXiv preprint arXiv:1802.08908","author":"Papernot Nicolas","year":"2018","unstructured":"Nicolas Papernot , Shuang Song , Ilya Mironov , Ananth Raghunathan , Kunal Talwar , and \u00dalfar Erlingsson . Scalable private learning with pate. arXiv preprint arXiv:1802.08908 , 2018 . Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and \u00dalfar Erlingsson. Scalable private learning with pate. arXiv preprint arXiv:1802.08908, 2018."},{"key":"e_1_2_1_49_1","volume-title":"Crawdad data set epfl\/mobility (v. 2009-02-24)","author":"Piorkowski Michal","year":"2009","unstructured":"Michal Piorkowski , Natasa Sarafijanovic-Djukic , and Matthias Grossglauser . Crawdad data set epfl\/mobility (v. 2009-02-24) , 2009 . Michal Piorkowski, Natasa Sarafijanovic-Djukic, and Matthias Grossglauser. Crawdad data set epfl\/mobility (v. 2009-02-24), 2009."},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/2935694.2935700"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/SRDS.2016.044"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2873950"},{"key":"e_1_2_1_53_1","volume-title":"C\u00e9dric Lauradoux, and Lionel Brunie. Differentially private location privacy in practice. arXiv preprint arXiv:1410.7744","author":"Primault Vincent","year":"2014","unstructured":"Vincent Primault , Sonia Ben Mokhtar , C\u00e9dric Lauradoux, and Lionel Brunie. Differentially private location privacy in practice. arXiv preprint arXiv:1410.7744 , 2014 . Vincent Primault, Sonia Ben Mokhtar, C\u00e9dric Lauradoux, and Lionel Brunie. Differentially private location privacy in practice. arXiv preprint arXiv:1410.7744, 2014."},{"key":"e_1_2_1_54_1","first-page":"539","volume-title":"C\u00e9dric Lauradoux, and Lionel Brunie. Time distortion anonymization for the publication of mobility data with high utility. In 2015 IEEE Trustcom\/BigDataSE\/ISPA","author":"Primault Vincent","year":"2015","unstructured":"Vincent Primault , Sonia Ben Mokhtar , C\u00e9dric Lauradoux, and Lionel Brunie. Time distortion anonymization for the publication of mobility data with high utility. In 2015 IEEE Trustcom\/BigDataSE\/ISPA , volume 1 , pages 539 -- 546 . IEEE , 2015 . Vincent Primault, Sonia Ben Mokhtar, C\u00e9dric Lauradoux, and Lionel Brunie. Time distortion anonymization for the publication of mobility data with high utility. In 2015 IEEE Trustcom\/BigDataSE\/ISPA, volume 1, pages 539--546. IEEE, 2015."},{"key":"e_1_2_1_55_1","volume-title":"The TOR Project: https:\/\/www.torproject.org","author":"Roger Dingledine Nick Mathewson","year":"2006","unstructured":"Nick Mathewson Roger Dingledine . The TOR Project: https:\/\/www.torproject.org , 2006 . Nick Mathewson Roger Dingledine. The TOR Project: https:\/\/www.torproject.org, 2006."},{"key":"e_1_2_1_56_1","volume-title":"Generating optimal privacy-protection mechanisms via machine learning. arXiv preprint arXiv:1904.01059","author":"Romanelli Marco","year":"2019","unstructured":"Marco Romanelli , Catuscia Palamidessi , and Konstantinos Chatzikokolakis . Generating optimal privacy-protection mechanisms via machine learning. arXiv preprint arXiv:1904.01059 , 2019 . Marco Romanelli, Catuscia Palamidessi, and Konstantinos Chatzikokolakis. Generating optimal privacy-protection mechanisms via machine learning. arXiv preprint arXiv:1904.01059, 2019."},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/69.971193"},{"key":"e_1_2_1_58_1","volume-title":"Ananda Theertha Suresh, and H Brendan McMahan. Can you really backdoor federated learning? arXiv preprint arXiv:1911.07963","author":"Sun Ziteng","year":"2019","unstructured":"Ziteng Sun , Peter Kairouz , Ananda Theertha Suresh, and H Brendan McMahan. Can you really backdoor federated learning? arXiv preprint arXiv:1911.07963 , 2019 . Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and H Brendan McMahan. Can you really backdoor federated learning? arXiv preprint arXiv:1911.07963, 2019."},{"key":"e_1_2_1_59_1","volume-title":"Thompson and Charlie Warzel. How to Track President Trump","author":"Stuart","year":"2019","unstructured":"Stuart A. Thompson and Charlie Warzel. How to Track President Trump , 2019 . Stuart A. Thompson and Charlie Warzel. How to Track President Trump, 2019."},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2018.1700644"},{"key":"e_1_2_1_61_1","volume-title":"A classification of location privacy attacks and approaches. Personal and ubiquitous computing, 18(1):163--175","author":"Wernke Marius","year":"2014","unstructured":"Marius Wernke , Pavel Skvortsov , Frank D\u00fcrr , and Kurt Rothermel . A classification of location privacy attacks and approaches. Personal and ubiquitous computing, 18(1):163--175 , 2014 . Marius Wernke, Pavel Skvortsov, Frank D\u00fcrr, and Kurt Rothermel. A classification of location privacy attacks and approaches. Personal and ubiquitous computing, 18(1):163--175, 2014."},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-01721-6_5"},{"key":"e_1_2_1_63_1","volume-title":"Applied federated learning: Improving google keyboard query suggestions. arXiv preprint arXiv","author":"Yang Timothy","year":"1812","unstructured":"Timothy Yang , Galen Andrew , Hubert Eichner , Haicheng Sun , Wei Li , Nicholas Kong , Daniel Ramage , and Fran\u00e7oise Beaufays . Applied federated learning: Improving google keyboard query suggestions. arXiv preprint arXiv : 1812 .02903, 2018. Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, and Fran\u00e7oise Beaufays. Applied federated learning: Improving google keyboard query suggestions. arXiv preprint arXiv: 1812.02903, 2018."},{"issue":"2","key":"e_1_2_1_64_1","first-page":"32","article-title":"Geolife: A collaborative social networking service among user, location and trajectory","volume":"33","author":"Zheng Yu","year":"2010","unstructured":"Yu Zheng , Xing Xie , Wei-Ying Ma , Geolife: A collaborative social networking service among user, location and trajectory . IEEE Data Eng. Bull. , 33 ( 2 ): 32 -- 39 , 2010 . Yu Zheng, Xing Xie, Wei-Ying Ma, et al. Geolife: A collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull., 33(2):32--39, 2010.","journal-title":"IEEE Data Eng. Bull."},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/1032222.1032261"}],"container-title":["Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3463502","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3463502","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:31:28Z","timestamp":1750195888000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3463502"}},"subtitle":["Enforcing Location Privacy through Re-identification Risk Assessment: A Federated Learning Approach"],"short-title":[],"issued":{"date-parts":[[2021,6,23]]},"references-count":64,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,6,23]]}},"alternative-id":["10.1145\/3463502"],"URL":"https:\/\/doi.org\/10.1145\/3463502","relation":{},"ISSN":["2474-9567"],"issn-type":[{"value":"2474-9567","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,23]]},"assertion":[{"value":"2021-06-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}