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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It is possible to reverse engineer anonymization strategies by combining datasets unless the data are fully de-identified.
“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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Mestari, S. Z. E., Lenzini, G., & Demirci, H. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103605 · Cited by 180 · see citing works
“Furthermore, sensitive attributes concealing techniques are susceptible to deanonymisation attacks (Narayanan and Shmatikov, 2019; De Montjoye et al., 2013).”
BackgroundLinking Users Across Domains with Location Data
Riederer, C., Kim, Y., Chaintreau, A., et al. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872427.2883002 · Cited by 161 · see citing works
Mobile phone location data for disasters: A review from natural hazards and epidemics
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Machine learning for geographically differentiated climate change mitigation in urban areas
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“Due to the regularity of human mobility [10, 18], the visited locations of one user during his or her daily life are limited to a small sub-set of the whole location set in the city.”
BackgroundNo place to hide? The ethics and analytics of tracking mobility using mobile phone data
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Data analytics in a privacy-concerned world
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The Measles Vaccination Narrative in Twitter: A Quantitative Analysis
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Ethical machines: The human-centric use of artificial intelligence
Lepri, B., Oliver, N., & Pentland, A. (2021) · iScience · doi:10.1016/j.isci.2021.102249 · Cited by 149 · see citing works
“For example, de Montjoye et al. (de Montjoye et al., 2013a, 2015) have demonstrated how unique mobility and shopping behaviors are for each individual.”
BackgroundInfluentialAn Accounting Information Systems Perspective on Data Analytics and Big Data
Huerta, E., & Jensen, S. (2017) · Journal of Information Systems · doi:10.2308/isys-51799 · Cited by 153 · see citing works
A Non-Parametric Generative Model for Human Trajectories
Ouyang, K., Shokri, R., Rosenblum, D. S., & Yang, W. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/530 · Cited by 145 · see citing works
“…many reasons: (1) Human trajectories are extremely high dimensional; (2) the trajectory of each individual is very unique in the geographical space [De Montjoye et al. , 2013]; nevertheless, (3) human mobilities are similar in an underlying semantic space, which gives meaning to a trajectory…”
BackgroundSpatio-Temporal Analytics for Exploring Human Mobility Patterns and Urban Dynamics in the Mobile Age
Gao, S. (2014) · Spatial Cognition and Computation · doi:10.1080/13875868.2014.984300 · Cited by 141 · see citing works
Tracking Human Mobility Using WiFi Signals
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Privacy-Preserving Crowd-Sourced Statistical Data Publishing with An Untrusted Server
Wang, Z., Pang, X., Chen, Y., et al. (2018) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2018.2861765 · Cited by 138 · see citing works
“A recent study [7] showed that with some outside informa-”
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All your location are belong to us
Li, M., Zhu, H., Gao, Z., Chen, S., Yu, L., Hu, S., & Ren, K. (2014) · ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc) · doi:10.1145/2632951.2632953 · Cited by 129 · see citing works
Privacy Issues and Data Protection in Big Data: A Case Study Analysis under GDPR
Gruschka, N., Mavroeidis, V., Vishi, K., & Jensen, M. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622621 · Cited by 135 · see citing works
“Other well-known cases include identification of individuals from internet search terms [12], anonymized DNA [13] and mobility data [14].”
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“Limited by data collection techniques [16], [17] and privacy issues [18], it is common to encounter the problem of missing spatial flows.”
BackgroundDP-WHERE: Differentially private modeling of human mobility
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Protection Goals for Privacy Engineering
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Privacy in mobile participatory sensing: Current trends and future challenges
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Network Structure Inference, A Survey
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Utility-Aware Synthesis of Differentially Private and Attack-Resilient Location Traces
Gürsoy, M. E., Liu, L., Truex, S., Yu, L., & Wei, W. (2018) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3243734.3243741 · Cited by 127 · see citing works
“First, since trajectories are often highdimensional and unique [17, 48], there may exist skewed instances that are easily singled out and mapped to particular users with high confidence.”
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Where You Are Is Who You Are: User Identification by Matching Statistics
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Differentially Private and Utility Preserving Publication of Trajectory Data
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“First, recent studies show that human mobility is unique and identifiable, and its identifiability decreases only slightly despite spatial coarsening [4], [5].”
Secure Automated Valet Parking: A Privacy-Preserving Reservation Scheme for Autonomous Vehicles
Huang, C., Lu, R., Lin, X., et al. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2870167 · Cited by 118 · see citing works
“These “side-information” could be linked to identify the unique human [7].”
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“Previous studies [5], [6] shows that achieving k-anonymity only causes huge data utility loss because of utilizing anonymization techniques like generalization and suppression to reduce trajectory spatio-temporal uniqueness.”
Background
- 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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