Uniqueness in the City

We investigate the potential for privacy leaks when users reveal their nearby Points-of-Interest (POIs). Specifically, we investigate whether and how a person's location can be reverse-engineered when that person simply reveals their nearby POI types (e.g. 2 schools and 3 restaurants). We approach our analysis by introducing a "Location Re-identification" algorithm that is computationally efficient. Using data from Open Street Map, we conduct our analysis on datasets of multiple representative cities: New York City, Melbourne, Vancouver, Zurich and Shanghai. Our analysis indicates that urban morphology has a clear link to location privacy, and highlights a number of urban factors that contribute to location privacy. Our findings can be used in any systems or platforms where users reveal their proximal POIs, such as recommendation systems, advertising platforms, and appstores.

[1]  Yu Zheng,et al.  U-Air: when urban air quality inference meets big data , 2013, KDD.

[2]  Nadia Magnenat-Thalmann,et al.  Time-aware point-of-interest recommendation , 2013, SIGIR.

[3]  K. Axhausen.  Can we ever obtain the data we would like to have , 1998 .

[4]  Kevin Lynch,et al.  The Image of the City , 1960 .

[5]  Gao Cong,et al.  Graph-based Point-of-interest Recommendation with Geographical and Temporal Influences , 2014, CIKM.

[6]  César A. Hidalgo,et al.  Unique in the Crowd: The privacy bounds of human mobility , 2013, Scientific Reports.

[7]  Souneil Park,et al.  MobInsight: Understanding Urban Mobility with Crowd-Powered Neighborhood Characterizations , 2016, 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).

[8]  Hui Xiong,et al.  Point-of-Interest Recommendation in Location Based Social Networks with Topic and Location Awareness , 2013, SDM.

[9]  Hui Xiong,et al.  Discovering Urban Functional Zones Using Latent Activity Trajectories , 2015, IEEE Transactions on Knowledge and Data Engineering.

[10]  Chunyan Miao,et al.  Personalized point-of-interest recommendation by mining users' preference transition , 2013, CIKM.

[11]  Y. de Montjoye,et al.  Unique in the shopping mall: On the reidentifiability of credit card metadata , 2015, Science.

[12]  K. Axhausen,et al.  Activity‐based approaches to travel analysis: conceptual frameworks, models, and research problems , 1992 .

[13]  A. M. Voorhees,et al.  A general theory of traffic movement , 2013 .

[14]  Patrick Tracy McGowen,et al.  Evaluating the Potential To Predict Activity Types from GPS and GIS Data , 2007 .

[15]  Hui Xiong,et al.  Characterizing the life cycle of point of interests using human mobility patterns , 2016, UbiComp.

[16]  M. Wegener.  Operational Urban Models State of the Art , 1994 .

[17]  Donghan Yu,et al.  Smartphone App Usage Prediction Using Points of Interest , 2017, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[18]  Xing Xie,et al.  Where to find my next passenger , 2011, UbiComp '11.

[19]  Jing Yang,et al.  Informative Yet Unrevealing: Semantic Obfuscation for Location Based Services , 2015, GeoPrivacy@SIGSPATIAL.

[20]  Yoshihiko Suhara,et al.  Probabilistic identification of visited point-of-interest for personalized automatic check-in , 2014, UbiComp.

[21]  Hui Xiong,et al.  Learning geographical preferences for point-of-interest recommendation , 2013, KDD.

[22]  Hui Xiong,et al.  A Cocktail Approach for Travel Package Recommendation , 2014, IEEE Transactions on Knowledge and Data Engineering.

[23]  Kai Zhao,et al.  Beyond K-Anonymity: Protect Your Trajectory from Semantic Attack , 2017, 2017 14th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON).

[24]  Mor Naaman,et al.  Generating diverse and representative image search results for landmarks , 2008, WWW.

[25]  Gabriel Ghinita,et al.  Privacy for Location-based Services , 2013, Privacy for Location-based Services.

[26]  K. Axhausen,et al.  Observing the rhythms of daily life , 2000 .

[27]  Michael Batty,et al.  Modeling urban growth with GIS based cellular automata and least squares SVM rules: a case study in Qingpu–Songjiang area of Shanghai, China , 2016, Stochastic Environmental Research and Risk Assessment.

[28]  Mao Ye,et al.  Exploiting geographical influence for collaborative point-of-interest recommendation , 2011, SIGIR.

[29]  Yanchi Liu,et al.  Diagnosing New York city's noises with ubiquitous data , 2014, UbiComp.

[30]  Chi-Yin Chow,et al.  Privacy in location-based services: a system architecture perspective , 2009, SIGSPACIAL.

[31]  Gang Yu,et al.  Predicting human activities using spatio-temporal structure of interest points , 2012, ACM Multimedia.

[32]  Yanchi Liu,et al.  Point-of-Interest Demand Modeling with Human Mobility Patterns , 2017, KDD.

[33]  Zhiyong Hu,et al.  Modeling urban growth in Atlanta using logistic regression , 2007, Comput. Environ. Urban Syst..

[34]  Hui Xiong,et al.  Real Estate Ranking via Mixed Land-use Latent Models , 2015, KDD.

[35]  E. J. Manley,et al.  Shortest path or anchor-based route choice: a large-scale empirical analysis of minicab routing in London , 2015 .

[36]  Daqing Zhang,et al.  Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[37]  Bin Guo,et al.  Personalized Travel Package With Multi-Point-of-Interest Recommendation Based on Crowdsourced User Footprints , 2016, IEEE Transactions on Human-Machine Systems.

[38]  M. Boarnet,et al.  Retrofitting the Suburbs to Increase Walking: Evidence from a Land-use-Travel Study , 2011, Urban studies.

[39]  Zhe Zhu,et al.  What's Your Next Move: User Activity Prediction in Location-based Social Networks , 2013, SDM.

[40]  Slava Kisilevich,et al.  P-DBSCAN: a density based clustering algorithm for exploration and analysis of attractive areas using collections of geo-tagged photos , 2010, COM.Geo '10.

[41]  Xiaoming Fu,et al.  Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data , 2017, WWW.

[42]  John Krumm,et al.  A survey of computational location privacy , 2009, Personal and Ubiquitous Computing.