Inference Attacks on Location Tracks

By John Krumm. International Conference on Pervasive Computing, 2007

688 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Epidemiology, Transportation

View Paper: https://www.semanticscholar.org/paper/5fd3096e84bc09635923a75fd4044ab618ec7861

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Citations in the corpus

Wireless Sensor Networks for Healthcare (2010, background): “Similarly, Krumm has shown that location traces can be de-anonymized via statistical analysis to infer subjects’ home location with high probability [44], which then can be used to reveal their identity using information that is freely available on the web such as reverse”
Privacy and accountability for location-based aggregate statistics (2011, background): “Some previous work ensures that clients upload data free of identi-ﬁers (they upload approximate time, location, and data sample), but even when all data is anonymized, a considerable amount of private location information can still leak due to side information.”
Understanding and capturing people’s privacy policies in a mobile social networking application (2009, methodology): “For example, Krumm presented several techniques for determining the home address of an individual, given location data from volunteer users [17].”
PEIR, the personal environmental impact report, as a platform for participatory sensing systems research (2009, methodology): “We found that in order to protect significant destinations, the spatial rounding/noise addition has to be significant (at least 100 meter addition), which conforms with the results shown in [49].”
A survey on privacy in mobile participatory sensing applications (2011, methodology): “Additionally, even when pseudonyms are used, the tasking server may infer the identity of the participants by tracking their locations over multiple downloads, as it may expose the location of their workplaces and homes [70].”
Protecting Locations with Differential Privacy under Temporal Correlations (2014, background): “However, anonymity or ad hoc uncertainty based techniques do not always provide sufficient privacy protection [22, 37].”
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].”
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 ”
Mix-Zones for Location Privacy in Vehicular Networks (2007, background): “For example, an adversary can deduce from mobility traces the home and work place of a driver, and hence his real world identity [16].”
Anonysense: privacy-aware people-centric sensing (2008, background): “We should note that simply suppressing or anonymizing the node’s identity in reports [36] is insufficient to protect users’ privacy—if multiple reports from nodes can be linked as being from the same user, they may reveal the identity of the user [24].”
Quantifying and protecting location privacy (2015, neutral): “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].”
VTrack: accurate, energy-aware road traffic delay estimation using mobile phones (2009, background): “A number of recent works show how to protect locational privacy while collecting vehicular traffic data [14, 19, 12] and in GPS traces [15].”
Defending against Sybil Devices in Crowdsourced Mapping Services (2015, background): “Location-based services face various threats, ranging from rogue users reporting fake GPS [11, 22], to malicious parties compromising user privacy [15, 26, 27].”
When Machine Learning Meets Privacy (2020, background): “anonymisation is reversed. Such kind of attack is illustrated in Fig.6(a). (2) Inference attack: This type of attack aims at “analyzing data in order to illegitimately gain knowledge about a subject” [70]. Such an attack is illustrated in Fig.6(b). (3) Linkage attack: The adversary aims to achieve a”
On the Anonymity of Home/Work Location Pairs (2009, background): “This definition of privacy is stronger than that described by Krumm in [5], which considers re-identification successful only when a link is positively established.”
What Does The Crowd Say About You? Evaluating Aggregation-based Location Privacy (2017, background): “time, however, large-scale collection of individuals’ whereabouts prompts serious privacy concerns, as location data may reveal one’s occupation, lifestyle, as well as political and religious beliefs [25,33]. A possible approach toward mitigating these concerns is to anonymize location traces prior ”
Optimal Geo-Indistinguishable Mechanisms for Location Privacy (2014, background): “While location-based systems (LBSs) have demonstrated to provide enormous benefits to individuals and society, these benefits come at the cost of users’ privacy: as discussed in [1, 2, 3], location data can be easily linked to a variety of other information about an individual, and expose sensitive ”