A survey of computational location privacy

By John Krumm. Personal and Ubiquitous Computing, 2009

756 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/a8236d4ab67c3010c5e3ec2a8d0b966571f8f51b

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Citations in the corpus

Quantifying and protecting location privacy (2015, neutral): “Yet, in the field of location privacy, notwithstanding many contributions from different disciplines (such as databases,mobile networks, andubiquitous computing [16]) for protecting location privacy, the lack of a unified and generic formal framework for specifying pro-”
Understanding and capturing people’s privacy policies in a mobile social networking application (2009, background): “Krumm provides a comprehensive overview of the state of the art in computational privacy [18].”
Pseudonym Schemes in Vehicular Networks: A Survey (2015, background): “Since location can be specified as a single coordinate, one way to measure location privacy is by how much an attacker might know about this coordinate [7].”
Geo-indistinguishability: differential privacy for location-based systems (2012, background): “There are excellent works and surveys [37, 26, 34] that summarize the different threats, methods, and guarantees in the context of location privacy.”
Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices (2015, background): “In particular, preserving privacy of users’ locations has been studied by many researchers (see [23] for a survey).”
Mobile crowdsensing: current state and future challenges (2011, background): “For example, GPS sensor readings can be utilized to infer private information about the individual, such as the routes they take during their daily commutes, home, and work locations [9].”
Privacy in the Internet of Things: threats and challenges (2014, background): “Extensive privacy research has been conducted focussing mostly on location-based services [52, 53, 54], detection of privacy breaches [55, 56] and privacy aware architectures for participatory sensing [57, 58].”
InnerCircle: A parallelizable decentralized privacy-preserving location proximity protocol (2015, background): “A major challenge is to address the privacy of LBS without hampering the utility of the services [19], [30].”
Technical Privacy Metrics (2015, background): “Metrics for location privacy are discussed by Shokri et al. [2010a] and Krumm [2009].”
Quantifying Location Privacy: The Case of Sporadic Location Exposure (2011, background): “Krumm [13] provides a literature survey of computational location privacy.”
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].”