On the Anonymity of Home/Work Location Pairs

By Philippe Golle, Kurt Partridge. 2009

420 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science

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Citations in the corpus

Knock Knock, Who's There? Membership Inference on Aggregate Location Data (2017, background): “Previous location privacy research taking into account traces or profiles of single users [7, 12, 16, 24, 28, 37] does not apply to our problem, which focuses on aggregate location statistics.”
Human mobility modeling at metropolitan scales (2012, background): “Partridge.”
DP-WHERE: Differentially private modeling of human mobility (2013, background): “Several recent papers have characterized the privacy risks of releasing location data, in each case demonstrating the ability to re-identify individual information from geospatial data sets [11, 23, 31].”
Study : Privacy Preserving Release of Spatio-temporal Density in Paris (2014, background): “Several recent studies have demonstrated the privacy risks of releasing location data by re-identifying individuals from geospatial data sets [8, 28, 13].”
Location Privacy via Private Proximity Testing (2011, background): “This approach has several potential limitations including the highly identifying nature of location information [16] and the limited location resolution resulting from the obfuscation or quantization needed for anonymization.”
Optimal Geo-Indistinguishable Mechanisms for Location Privacy (2014, background): “Categories and Subject Descriptors C.2.0 [Computer–Communication Networks]: General— Security and protection; K.4.1 [Computers and Society]: Public Policy Issues—Privacy Keywords Location privacy; Location obfuscation; Geo-indistinguishability; Differential privacy; Linear optimization”
Unraveling an old cloak: k-anonymity for location privacy (2010, background): “For example, the adversary may have access to publicly available information on users’ homes and work places [5] and knows that, with a high probability, users will be at home during the night, and at their work places during office hours.”
Protecting location privacy: optimal strategy against localization attacks (2012, background): “The whereabouts users’ disclose to the service provider expose aspects of their private life that is not apparent at first, but can be inferred from the revealed location data [8, 11, 18].”
Engineering Privacy by Design (2011, background): “Even if the traces are anonymized, the driver’s identity can be inferred from the traces themselves [21, 28].”
PIR-Tor: Scalable Anonymous Communication Using Private Information Retrieval (2011, background): “In turn, these profiles can lead to the re-identification of users directly [16] or by combining them with publicly available databases [14, 30, 43].”
Plausible Deniability for Privacy-Preserving Data Synthesis (2017, background): “A range of inference attacks on, for example, AOL search log dataset [2], Netﬂix movie rating dataset [39], Genomic data [48, 22], location data [18, 46], and social networks data [40], shows that simple modiﬁcation of sensitive data by removing identiﬁers or by generalizing/suppressing data…”
Pseudonym Schemes in Vehicular Networks: A Survey (2015, methodology): “Golle and Partridge [48] analyze data from the U.S. Census and show that for the average person, knowing their approximate home and work locations—to a block level—identifies them uniquely.”
Quantifying Location Privacy: The Case of Sporadic Location Exposure (2011, background): “Golle and Partridge [7], Beresford and Sta-jano [2], Hoh et al. [10], and Krumm [12] use diﬀerent techniques to show that users can be identiﬁed by inferring where they spend most of their time (notably their home and workplace).”
Quantifying and protecting location privacy (2015, positive): “The disclosure of users’ whereabouts to the LBS exposes aspects of their private life that is not apparent at first, but can be inferred from the revealed location data [10, 12, 15, 23, 25].”
TapLogger: inferring user inputs on smartphone touchscreens using on-board motion sensors (2012, background): “Besides the location tracking attacks [16, 18, 21], recent attacks show that the video camera might be controlled by a malware to stealthily record the surrounding when a user enters a building[42] that requires security clearance, credit card and pin numbers can be stolen by a trojan malware which ”