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Unique in the Crowd: The privacy bounds of human mobility
Open accessAbstract
We study fifteen months of human mobility data for one and a half million individuals and find that human mobility traces are highly unique. In fact, in a dataset where the location of an individual is specified hourly, and with a spatial resolution equal to that given by the carrier's antennas, four spatio-temporal points are enough to uniquely identify 95% of the individuals. We coarsen the data spatially and temporally to find a formula for the uniqueness of human mobility traces given their resolution and the available outside information. This formula shows that the uniqueness of mobility traces decays approximately as the 1/10 power of their resolution. Hence, even coarse datasets provide little anonymity. These findings represent fundamental constraints to an individual's privacy and have important implications for the design of frameworks and institutions dedicated to protect the privacy of individuals.
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“Indeed, it is clear that it is possible to reverse engineer anonymization strategies by combing and combining datasets [56,57] unless the data are fully de-identified.”
From The ethics of smart cities and urban science (2016) · cites this paper as [57]
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Cites
k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
SWEENEY, L. (2002) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488502001648 · Cited by 8,571 · cited 1×
“For instance, in one study, a medical database was successfully combined with a voters list to extract the health record of the governor of Massachusetts 27 .”
BackgroundNewman, M. (2010) · doi:10.1093/acprof:oso/9780199206650.001.0001 · Cited by 6,595
Understanding individual human mobility patterns
González, M. C., Hidalgo, C. A., & Barabási, A.-Ĺ. (2008) · Nature · doi:10.1038/nature06958 · Cited by 6,186
Self-citation(2011) · Cited by 5,203
Warren, S. D., & Brandeis, L. D. (1890) · University of Washington Press eBooks · doi:10.1515/9780295803661-014 · Cited by 4,092
Nonlinear Regression Analysis and Its Applications
Bates, D. M., & Watts, D. G. (1988) · Wiley series in probability and statistics · doi:10.1002/9780470316757 · Cited by 4,017
Limits of Predictability in Human Mobility
Song, C., Qu, Z., Blumm, N., & Barabási, A.-L. (2010) · Science · doi:10.1126/science.1177170 · Cited by 3,234 · cited 2×
“B] have been used in the past for research purposes 4–18 and to provide personalized services to users 19 .”
BackgroundInfluentialWarren, S. D., & Brandeis, L. D. (1890) · Harvard Law Review · doi:10.2307/1321160 · Cited by 2,760
Robust De-anonymization of Large Sparse Datasets
Narayanan, A., & Shmatikov, V. (2008) · 2008 IEEE Symposium on Security and Privacy (sp 2008) · doi:10.1109/sp.2008.33 · Cited by 2,372
The origin of bursts and heavy tails in human dynamics
Barabási, A.-L. (2005) · Nature · doi:10.1038/nature03459 · Cited by 2,210
Flagged first, then by citations
Research works printed but not linked: 2
- Clanchy, M. T. From Memory to Written Records England 1066–1307 (Harvard University Press, Cambridge, 1979).
- Newman, M. E. J. Networks: An Introduction (Oxford University Press, New York, 2010).
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Items 501–600 of 1,000
Privacy Risk and Preservation in Contact Tracing of COVID‐19
Wang, D., & Liu, F. (2020) · CHANCE · doi:10.1080/09332480.2020.1820252 · Cited by 17
Nationwide deployment and operation of a virtual arrival detection system in the wild
Ding, Y. C., Yang, Y., Jiang, W., et al. (2021) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/3452296.3472911 · Cited by 17
“, K=1 day) can provide enough protection is that to successfully de-anonymize the identity in the leaked data-set, the trajectory collected by the attacker must have multiple overlapping spatial points at different locations with the leaked data-set [39], which is extremely difficult to achieve based on data collected…”
BackgroundMethodInfluentialAm I Private and If So, how Many?
Franzén, D., von Voigt, S. N., Sörries, P., Tschorsch, F., & Müller-Birn, C. (2022) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3548606.3560693 · Cited by 23
“However, research has shown, for example, that simply removing user identifiers is insufficient for anonymizing data adequately [49] and especially concerning mobility data, 4 randomly known data-points are enough to identify 95 % of mobility traces uniquely [21].”
BackgroundReidentification Risk in Panel Data: Protecting fork-Anonymity
Li, S., Schneider, M. J., Yu, Y., & Gupta, S. (2022) · Information Systems Research · doi:10.1287/isre.2022.1169 · Cited by 20
ST-TrajGAN: A synthetic trajectory generation algorithm for privacy preservation
Ma, X., Ding, Z., & Zhang, X. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.07.011 · Cited by 17
Li, X., Xu, H., Chen, J., et al. (2016) · Scientific Reports · doi:10.1038/srep32522 · Cited by 17
Privacy violations in Riga open data public transport system
Lavrenovs, A., & Podins, K. (2016) · IEEE Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE) · doi:10.1109/aieee.2016.7821808 · Cited by 17
Kandappu, T., Misra, A., Cheng, S.-F., Tandriansyah, R., & Lau, H. C. (2018) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3191748 · Cited by 17
“This higher fraction also raises a question of users being re-identied due to the uniqueness [10] and low-diversity of their trajectories (similar to the diversity issue reported in an urban campus environment in [29]).”
BackgroundCollecting, exploring and sharing personal data: Why, how and where
Estrada-Galiñanes, V., & Wac, K. (2019) · Data Science · doi:10.3233/ds-190025 · Cited by 18
Methods for Inferring Route Choice of Commuting Trip From Mobile Phone Network Data
Sakamanee, P., Phithakkitnukoon, S., Smoreda, Z., et al. (2020) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi9050306 · Cited by 17
“Though the CDR data is not as detailed as GPS tracking data, it is worth noting that there is still a privacy concern even if they are anonymized when analyzed rigorously with additional outside information [11].”
BackgroundImpact of natural disasters on consumer behavior: Case of the 2017 El Niño phenomenon in Peru
Alatrista-Salas, H., Gauthier, V., Núñez-del-Prado, M., & Becker, M. (2021) · PLoS ONE · doi:10.1371/journal.pone.0244409 · Cited by 21
“The dataset provided contains enough information such that the anonymized ID could be subject to data reunification [41].”
BackgroundChronemic urgency in everyday digital communication
Kalman, Y. M., Ballard, D. I., & Aguilar, A. M. (2021) · Time & Society · doi:10.1177/0961463x20987721 · Cited by 19
“Additionally, there are many other processes and stimuli to which human temporal cycles might become entrained — including routine and regular communication that becomes patterned in particular ways, as reported by Montjoye et al. (2013).”
BackgroundGrowing synthetic data through differentially-private vine copulas
Gambs, S., Ladouceur, F., Laurent, A., & Roy-Gaumond, A. (2021) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2021-0040 · Cited by 17
“In addition, the one-hot encoding could not be tested on Texas as the number of attributes augment from 17 to 5375, due to the increase in the computational burden as our approach scales linearly with the number of attributes.”
BackgroundSmartphones for musculoskeletal research – hype or hope? Lessons from a decennium of mHealth studies
Beukenhorst, A. L., Druce, K. L., & Cock, D. D. (2022) · BMC Musculoskeletal Disorders · doi:10.1186/s12891-022-05420-8 · Cited by 18
“data, providing higher accuracy while preserving privacy [75].”
BackgroundMethodHohenstatt, R., & Käsbauer, M. (2014) · Journal of Real Estate Research · doi:10.1080/10835547.2014.12091387 · Cited by 17
Privacy-enhancing aggregation of Internet of Things data via sensors grouping
Bennati, S., & Pournaras, E. (2018) · Sustainable Cities and Society · doi:10.1016/j.scs.2018.02.013 · Cited by 18
South Korea: in the midst of a privacy reform centered on data sharing
Kim, H., Kim, S. Y., & Joly, Y. (2018) · Human Genetics · doi:10.1007/s00439-018-1920-1 · Cited by 18
SwapMob: Swapping Trajectories for Mobility Anonymization
Salas, J., Megías, D., & Torra, V. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99771-1_22 · Cited by 17
“A different approach regarding the possibility of reidentification and the (im)possibility of protection, is in [17], where they measure the uniqueness of human mobility traces depending on their resolution and the available outside information, assuming that an adversary knows p random…”
BackgroundResultModeling and Prediction of Ride-Sharing Utilization Dynamics
Altshuler, T., Altshuler, Y., Katoshevski, R., et al. (2019) · Journal of Advanced Transportation · doi:10.1155/2019/6125798 · Cited by 17
Trajectory-Based Spatiotemporal Entity Linking
Jin, F., Hua, W., Zhou, T., Xu, J., Francia, M., Orłowska, M. E., & Zhou, X. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3036633 · Cited by 18
“As discovered in [1], 4 locations can identify 95% of the mobile phone users.”
BackgroundMethodResultInfluentialLearning the complexity of urban mobility with deep generative network
Yuan, Y., Ding, J., Jin, D., & Li, Y. (2025) · PNAS Nexus · doi:10.1093/pnasnexus/pgaf081 · Cited by 22
A review of human mobility: Linking data, models, and real-world applications
Du, Y., Aoki, T., & Fujiwara, N. (2025) · Journal of Computational Social Science · doi:10.1007/s42001-025-00414-7 · Cited by 21
Wearable Biosensing and Machine Learning for Data-Driven Training and Coaching Support
Madrigal-Cerezo, R., Sanz, N. D., & Martín-Rodríguez, A. (2026) · Biosensors · doi:10.3390/bios16020097 · Cited by 19
“…just four spatiotemporal points were sufficient to re-identify 95% of athletes in motion-tracking datasets, even after de-identification procedures [164], and Piciucco et al. found that individual users could be correctly re-identified with over 90% accuracy using heart rate variability and…”
Xiao, Q., Chen, J., Yu, L., et al. (2014) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2660267.2662367 · Cited by 16
Public anonymity and the connected world
Doyle, T., & Veranas, J. (2014) · Ethics and Information Technology · doi:10.1007/s10676-014-9346-5 · Cited by 15
de Montjoye, Y.-A., & Pentland, A. (2016) · Science · doi:10.1126/science.aaf1578 · Cited by 16
A contextual collaborative approach for app usage forecasting
Wang, Y., Yuan, N. J., Sun, Y., et al. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2971648.2971729 · Cited by 16
Establishing phone-pair co-usage by comparing mobility patterns
Bosma, W., Dalm, S., van Eijk, E., el Harchaoui, R., Rijgersberg, E., Tops, H. T., Veenstra, A., & Ypma, R. J. F. (2019) · Science & Justice · doi:10.1016/j.scijus.2019.10.005 · Cited by 16
“[2] is that the spatiotemporal patterns that emerge as a result of a mobile phone’s activity can be used to identify its user.”
BackgroundMeasuring Membership Privacy on Aggregate Location Time-Series
Pyrgelis, A., Troncoso, C., & Cristofaro, E. D. (2020) · Proceedings of the ACM on Measurement and Analysis of Computing Systems · doi:10.1145/3392154 · Cited by 19
“[11] measure the uniqueness of human mobility in a Call Detail Records (CDR) dataset, finding that four spatio-temporal points are enough to uniquely identify 95% of the individuals, while Rossi et al.”
BackgroundMethodInfluentialAn Overview of De-Identification Techniques and Their Standardization Directions
Youm, H. Y. (2020) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2019ici0002 · Cited by 17
“For example, a study [2] showed that people could be iden-tified by their “mobility traces” (a record of locations and times that the person or vehicle visited).”
BackgroundBetween privacy and security: the factors that drive intentions to use cyber-security applications
Chassidim, H. S., Perentis, C., Toch, E., & Lepri, B. (2020) · Behaviour and Information Technology · doi:10.1080/0144929x.2020.1781259 · Cited by 17
“One could think about the re-identi fi cation risks and thus use anonymization and aggregation techniques (de Montjoye et al. 2013).”
MethodReview and Critical Analysis of Privacy-Preserving Infection Tracking and Contact Tracing
Buchanan, W. J., Imran, M. A., Rehman, M. U., Zhang, L., Abbasi, Q. H., Chrysoulas, C., Haynes, D., Pitropakis, N., & Papadopoulos, P. (2020) · Frontiers in Communications and Networks · doi:10.3389/frcmn.2020.583376 · Cited by 16
“These mobility traces are unique and identify the users accurately giving information about home and work locations, visited places, and personality traits [68], [69].”
BackgroundPrivacy and Security in Smart Data Collection by Citizens
Choenni, S., Bargh, M. S., Roepan, C., et al. (2015) · Public administration and information technology · doi:10.1007/978-3-319-17620-8_19 · Cited by 16
Generic Pattern of Life and behaviour analysis
Craddock, R., Watson, D., & Saunders, W. (2016) · IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support · doi:10.1109/cogsima.2016.7497803 · Cited by 16
Anonymous or Not? Understanding the Factors Affecting Personal Mobile Data Disclosure
Perentis, C., Vescovi, M., Leonardi, C., Moiso, C., Musolesi, M., Pianesi, F., & Lepri, B. (2017) · ACM Transactions on Internet Technology · doi:10.1145/3017431 · Cited by 17
Spatio-Temporal Linkage over Location-Enhanced Services
Basik, F., Gedik, B., Etemoglu, C., et al. (2017) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2017.2711027 · Cited by 16
“Unlike our work, [34] does not consider the linkage problem, instead,”
BackgroundOn Privacy in Spatio-Temporal Data: User Identification Using Microblog Data
Seglem, E., Züfle, A. E., Stutzki, J., et al. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-64367-0_3 · Cited by 16
Hong, L., Wu, J., Frı́as-Martı́nez, E., Villarreal, A., & Frías-Martínez, V. (2019) · International Conference on Information and Communication Technologies and Development (ICTD) · doi:10.1145/3287098.3287119 · Cited by 15
A Survey on Privacy Issues in Mobile Social Networks
Sai, A. M. V. V., & Li, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3009691 · Cited by 19
“Similarly, authors in [39] were able to identify users from four spatio-temporal points.”
BackgroundPrivacy-enhancing Aggregation of Internet of Things Data via Sensors Grouping
Bennati, S., & Pournaras, E. (2018) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000245849 · Cited by 16
walk2friends: Inferring Social Links from Mobility ProfilesPreprint
Michael Backes, A., Humbert, M., Pang, J., & Zhang, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.08221 · Cited by 16
Automatic Tracking of Behavior With Smartphones: Potential for Behavior Change Interventions
Piwek, L., & Joinson, A. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-802690-8.00005-0 · Cited by 15
Asada, M., Yoshikawa, M., & Cao, Y. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-22479-0_9 · Cited by 15
GLOVE: Towards Privacy-Preserving Publishing of Record-Level-Truthful Mobile Phone Trajectories
Gramaglia, M., Fiore, M., Furno, A., et al. (2021) · ACM/IMS Transactions on Data Science · doi:10.1145/3451178 · Cited by 15
Sovereign Digital Consent through Privacy Impact Quantification and Dynamic Consent
Appenzeller, A., Hornung, M., Kadow, T., Krempel, E., & Beyerer, J. (2022) · Technologies · doi:10.3390/technologies10010035 · Cited by 15
Privacy-Preserving Synthetic Educational Data Generation
Vie, J.-J., Rigaux, T., & Minn, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16290-9_29 · Cited by 15
MapSafe : A complete tool for achieving geospatial data sovereignty
Sharma, P., Martin, M., & Swanlund, D. (2023) · Transactions in GIS · doi:10.1111/tgis.13094 · Cited by 15
Zhou, X., You, L., Zhong, S., et al. (2024) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2024.102130 · Cited by 15
Fujihara, A., & Miwa, H. (2014) · IEEE Annual Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac.2014.81 · Cited by 15
Robinson, S. C. (2017) · Digital Policy Regulation and Governance · doi:10.1108/dprg-05-2017-0018 · Cited by 16
Smartphones within Psychological Science
Ellis, D. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671408 · Cited by 14
Privacy and uniqueness of neighborhoods in social networks
Romanini, D., Lehmann, S., & Kivelä, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-94283-5 · Cited by 15
Tian, C., Xu, H., Lű, T., Jiang, R., & Kuang, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3071407 · Cited by 15
Privacy-Aware Human Mobility Prediction via Adversarial Networks
Zhan, Y., Haddadi, H., Kyllo, A., et al. (2022) · doi:10.1109/cphs56133.2022.9804533 · Cited by 15
“A mobility privacy study conducted by De Montjoye et al [8] illustrates that four spatio-temporal points are enough to identify 95% of the individuals in a certain granularity.”
InfluentialPrivacy pitfalls of releasing in-vehicle network data
Gazdag, A., Lestyán, S., Remeli, M., et al. (2022) · Vehicular Communications · doi:10.1016/j.vehcom.2022.100565 · Cited by 15
“of an individual’s daily trajectory identify 95% of all individuals uniquely [3]; this facilitates singling out.”
BackgroundWang, Y., & McArthur, D. P. (2018) · Transactions in GIS · doi:10.1111/tgis.12334 · Cited by 15
“This poses a privacy threat, as the complexity of location data makes it difficult to anonymize (Abul, Bonchi, & Nanni, 2008; De Montjoye et al., 2013).”
BackgroundThe computer for the 21st century: present security & privacy challenges
Oliveira, L. B., Pereira, F. M. Q., Misoczki, R., Aranha, D. F., Borges, F., Nogueira, M., Wangham, M. S., Wu, M. H., & Liu, J. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0095-2 · Cited by 16
“It seems safe, but only 4 points marking the position on the time are enough to identify uniquely 95% of the cellphone users [190].”
BackgroundFarokhi, F. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2903660 · Cited by 15
“Although popularly adopted, anonymization has been proved to be insufficient for privacy preservation [1]–[3].”
BackgroundNowcasting GDP Growth by Reading Newspapers
Bortoli, C., Combes, S., & Renault, T. (2019) · Economie et Statistique / Economics and Statistics · doi:10.24187/ecostat.2018.505d.1964 · Cited by 16
The path most travelled: Mining road usage patterns from massive call dataPreprint
Toole, J. L., Çolak, S., Alhasoun, F., Evsukoff, A. G., & González, M. C. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1403.0636 · Cited by 17
How to Avoid Reidentification with Proper AnonymizationPreprint
Sánchez, D., Mart́ınez, S., & Domingo‐Ferrer, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.01113 · Cited by 15
McNicholas, C., & Mass, C. F. (2021) · Weather and Forecasting · doi:10.1175/waf-d-20-0222.1 · Cited by 14
A comprehensive review of current trends, challenges, and opportunities in text data privacy
Shahriar, S., Dara, R. A., & Akalu, R. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104358 · Cited by 19
Real-time streaming mobility analytics
Garzó, A., Benczúr, A. A., Sidló, C. I., et al. (2013) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2013.6691639 · Cited by 14
Kulkarni, V., Moro, A., & Garbinato, B. (2016) · International Workshop on GeoStreaming · doi:10.1145/3003421.3003424 · Cited by 14
Online Public Health Intelligence: Ethical Considerations at the Big Data Era
Mamiya, H., Shaban‐Nejad, A., & Buckeridge, D. L. (2017) · Lecture notes in social networks · doi:10.1007/978-3-319-68604-2_8 · Cited by 14
Quantifying Privacy Loss of Human Mobility Graph Topology
Manousakas, D., Cecilia Mascolo, C., Beresford, A. R., Chan, D., & Sharma, N. (2018) · Proceedings on Privacy Enhancing Technologies · doi:10.1515/popets-2018-0018 · Cited by 14
“Moreover, temporal resolution may also be deliberately decreased to improve anonymization [11] since previous work has demonstrated that sparsity in the temporal evolution of mobility can cause privacy breaches [6].”
BackgroundTo Return or to Explore: Modelling Human Mobility and Dynamics in Cyberspace
Hu, T., Xia, Y., & Luo, J. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313686 · Cited by 14
Privacy-preserving inference of social relationships from location data
Shahabi, C., Fan, L., Nocera, L. P., et al. (2015) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/2820783.2820880 · Cited by 14
He, X., Raval, N., & Machanavajjhala, A. (2016) · Proceedings of the VLDB Endowment · doi:10.14778/3007263.3007291 · Cited by 14
Internet network location privacy protection with multi-access edge computing
Zhang, P., Durrësi, M., & Arjan Durresi, A. (2020) · Computing · doi:10.1007/s00607-020-00860-3 · Cited by 14
“Other researches have pointed out that because human mobility trace is very unique [7], even completed anatomized data can still be used to extract patterns and identify individuals [15,17,32].”
BackgroundMethodBeaude, B. (2015) · Presses des Mines eBooks · doi:10.4000/books.pressesmines.2000 · Cited by 14
Open Data PrivacyPreprint
Green, B., Cunningham, G., Ekblaw, A., & Kominers, P. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2924751 · Cited by 15
Weggenmann, B., & Kerschbaum, F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.00904 · Cited by 14
“However, these methods are insufficient to protect the author’s identity: As the famous Netflix de-anonymization attack [31] and other studies [9, 16, 35, 42] have shown, the originator of data can be reidentified from the data itself.”
Data autonomy and privacy in the smart home: the case for a privacy smart home meta-assistant
Orlowski, A., & Loh, W. (2025) · AI & Society · doi:10.1007/s00146-025-02182-4 · Cited by 16
“In principle, de-anonymization is almost always technically possible—it is merely a question of effort (Montjoye et al. 2013).”
Yu, D., Tao, Q., Liu, Q., Jin, Y., Sun, Y., & Fu, P. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.105706 · Cited by 18
Near-Optimal Fingerprinting with Constraints
Gulyas, G. G., Ács, G., & Castelluccia, C. (2016) · Proceedings on Privacy Enhancing Technologies · doi:10.1515/popets-2016-0051 · Cited by 13
TULAM: trajectory-user linking via attention mechanism
Li, H., Cao, S., Chen, Y., Zhang, M., & Feng, D. (2023) · Science China Information Sciences · doi:10.1007/s11432-021-3673-6 · Cited by 13
Lin, Y. (2024) · Environment and Planning F · doi:10.1177/26349825231224029 · Cited by 13
“The literature on location privacy is expanding to address the impacts caused by the emergence of geospatial data and technologies (Armstrong and Ruggles, 2005; Banerjee, 2019; Clarke and Wigan, 2011; Curry, 1997a; De Montjoye et al., 2013; Dobson, 2009; Swanlund and Schuurman, 2016; Taylor, 2016).”
BackgroundIn Silico Human Mobility Data Science: Leveraging Massive Simulated Mobility Data (Vision Paper)
Züfle, A. E., Pfoser, D., Wenk, C., Crooks, A. T., Kavak, H., Anderson, T., Kim, J.-S., Holt, N., & DiAntonio, A. (2024) · ACM Transactions on Spatial Algorithms and Systems · doi:10.1145/3672557 · Cited by 13
“Thus, despite efforts to anonymize the data, privacy attacks remain a risk.”
BackgroundCao, Z., Liu, K., Jin, X., Li, N., Yin, L., & Lu, F. (2024) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2024.2381146 · Cited by 17
“More critically, individual trajectories contain sensitive private information and research has demonstrated that individuals can be readily re-identified through data extracted from just a few outside locations within the dataset (de Montjoye et al .”
Enhanced user data privacy with pay-by-data model
Wu, C., & Guo, Y. (2013) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2013.6691688 · Cited by 13
Chernyshev, M., Valli, C., & Johnstone, M. N. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2678463 · Cited by 13
Uniqueness Assessment of Human Mobility on Multi-Sensor Datasets
Boutet, A., & Mokhtar, S. B. (2021) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1145/3465481.3470474 · Cited by 13
Control de nuestros datos personales en la era del big data: El caso del rastreo web de terceros
Gonzalez, L. D. (2018) · Estudios Socio-Jurídicos · doi:10.12804/revistas.urosario.edu.co/sociojuridicos/a.6941 · Cited by 13
Ometov, A., Bezzateev, S. V., Davydov, V. V., Shchesniak, A., Mašek, P., Lohan, E. S., & Koucheryavy, Y. A. (2019) · Sensors · doi:10.3390/s19071603 · Cited by 14
“Note, it is possible to uniquely identify the node based on just four hours of the monitoring while having access to the position-related data provider in 95% cases [34] based on said digital activity ‘footprints’ [35].”
BackgroundChandrasekhar, R., Utts, J. M., Cohen, A. I., & Hayat, M. J. (2022) · The American Statistician · doi:10.1080/00031305.2022.2156612 · Cited by 13
“Our work was motivated from growing concerns regarding unethical uses of data (Narayanan and Shmatikov 2008; De Montjoye et al. 2013; Kramer, Guillory, and Hancock 2014; Xiao and Ma 2021).”
BackgroundInvestigating vulnerabilities in GSM security
Pannu, M., Bird, R., Gill, B., et al. (2015) · doi:10.1109/iemcon.2015.7344480 · Cited by 13
Location Privacy Breach: Apps Are Watching You in Background
Liu, D., Gao, X., & Wang, H. (2017) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2017.227 · Cited by 13
Minimising Information Loss on Anonymised High Dimensional Data with Greedy In-Memory Processing
Podlesny, N. J., Kayem, A. V. D. M., von Schorlemer, S., & Uflacker, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-98809-2_6 · Cited by 13
Toward Robust Interpretable Human Movement Pattern Analysis in a Workplace Setting
Booth, B. M., Feng, T., Jangalwa, A., et al. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683730 · Cited by 13
“Other works have shown that human movement patterns outdoors are unique for most individuals [7, 8] and also in some cases that their trajectories can be predicted from motion models and past observations [9, 10, 11].”
BackgroundPrivacy disclosure by de-anonymization using music preferences and selections
Hirschprung, R. S., & Leshman, O. (2021) · Telematics and Informatics · doi:10.1016/j.tele.2021.101564 · Cited by 14
Ashkouti, F., Khamforoosh, K., Sheikhahmadi, A., et al. (2021) · The Journal of Supercomputing · doi:10.1007/s11227-021-03958-3 · Cited by 13
On Information-theoretic Measures for Quantifying Privacy Protection of Time-series Data
Ma, C. Y. T., & Yau, D. K. Y. (2015) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/2714576.2714577 · Cited by 13
Who owns (or controls) health data?
Kahn, S. D., & Terry, S. F. (2024) · Scientific Data · doi:10.1038/s41597-024-02982-1 · Cited by 13
Generating Daily Activities with Need Dynamics
Yuan, Y., Ding, J., Wang, H., & Jin, D. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3637493 · Cited by 15
“To prove that the generated daily activity sequence does not leak individual privacy, we perform experiments from comprehensive aspects as follows: — Uniqueness Testing [13, 71].”
MethodSecuring Personally Identifiable Information: A Survey of SOTA Techniques, and a Way Forward
Makhdoom, I., Abolhasan, M., Lipman, J., Shariati, N., Franklin, D. R., & Piccardi, M. (2024) · IEEE Access · doi:10.1109/access.2024.3447017 · Cited by 12
Global and individual mobility pattern discovery based on hotspots
Yang, J., Zhang, X., Qiao, Y., et al. (2015) · International Conference on Communications · doi:10.1109/icc.2015.7249211 · Cited by 12
Adaptation patterns to high speed rail usage in Taiwan and China
Li, Y.-T., & Schmöcker, J. (2016) · Transportation · doi:10.1007/s11116-016-9679-5 · Cited by 12
Multi-agent urban transport simulations using OD matrices from mobile phone data
Anda, C., Ordóñez, S., & Fourie, P. J. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.04.139 · Cited by 12
Metadata
- Authors
- de Montjoye, Y.-A., Hidalgo, C. A., Verleysen, M., & Blondel, V. D.
- Affiliations
- Harvard University (US)
- UCLouvain (BE)
- Massachusetts Institute of Technology (US)
- Venue
- Scientific Reports
- Published
- 2013-03-25
- Volume
- 3
- Issue
- 1
- Pages
- 1376-1376
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC3607247
- OpenAlex
- W2115240023
- Semantic Scholar
- 7fe16a270f403048f78fa89a64410e570ae5c16f
- Cited by
- 1,645
- References
- 29
- Citations by year
- 2026: 96 · 2025: 86 · 2024: 92 · 2023: 96 · 2022: 133 · 2021: 156 · 2020: 191 · 2019: 153 · 2018: 152 · 2017: 124 · 2016: 124 · 2015: 119 · 2014: 89 · 2013: 33
- FWCI
- 134.68
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Scientific Reports (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-nd)
- Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Keywords
- human mobility, privacy, anonymity, spatiotemporal resolution, location data, privacy preservation
- MeSH terms
- Geography, Humans, Privacy, Databases as Topic, Cell Phone
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