Location Privacy via Private Proximity Testing

By Arvind Narayanan, Narendran Thiagarajan, Mugdha Lakhani, Michael Hamburg, Dan Boneh. 2011

392 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Secure Federated Submodel Learning (2019, background): “Compared with Private Set Intersection (PSI) [71]–[76] and Private Set union Cardinality (PSC) [77]– [79], which have received tremendous attention and also have seen several practical applications, such as in social networks [80], [81]; human genome testing [82]; locationbased services [83]; securi”
Strathprints Institutional Repository (unknown): “For example, PSI has been proposed as a building block in applications such as privacy preserving data mining [4], human genome research [6], homeland security [15], Botnet detection [34], social networks [33], location sharing [36] and cheater detection in online games [11].”
Scalable Private Set Intersection Based on OT Extension (2018, background): “…such as relationship path discovery in social networks [Mezzour et al. 2009], botnet detection [Nagaraja et al. 2010], testing of fully-sequenced human genomes [Baldi et al. 2011], proximity testing [Narayanan et al. 2011], or cheater detection in online games [Bursztein et al. 2011].”
More efficient oblivious transfer and extensions for faster secure computation (2013, unknown): “Applications include governmental law enforcement [9], sharing location data [41], and botnet detection [39].”
Faster Private Set Intersection Based on OT Extension (2014, methodology): “PSI is used for privacy-preserving computation of functionalities such as relationship path discovery in social networks [41], botnet detection [45], testing of fully-sequenced human genomes [3], proximity testing [48], or cheater detection in online games [11].”
Defending against Sybil Devices in Crowdsourced Mapping Services (2015, background): “Other researchers have proposed “peer-based” methods to authenticate collocated mobile devices [42, 51, 55, 31, 34].”
Privacy and accountability for location-based aggregate statistics (2011, result): “There has also been recent work by Narayanan et al. [31], Zhong et al. [44], and Puttaswamy and Zhao [36] on preserving location privacy in location-based social networking applications, an area also full of location privacy concerns.”
InnerCircle: A parallelizable decentralized privacy-preserving location proximity protocol (2015, background): “These attacks are orthogonal, and can be mitigated by using tamper-resistant location devices or strategies such as location tags [25].”