Enhancing Security and Privacy in Traffic-Monitoring Systems

By Baik Hoh, Marco Gruteser, Hui Xiong, Ansaf Ibrahem Alrabady. IEEE pervasive computing, 2006

345 citations recorded by the source corpus.

Subfields: Building and Construction, Electrical and Electronic Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/58441cae3ee88f7a86b9c507c2cc43a34f63a4aa

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In Building and Construction

In Electrical and Electronic Engineering

In Transportation

Citations in the corpus

Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experiment (2009, methodology): “For example, pseudo-anonymous trajectories have been combined with free, publicly available data sets to determine the addresses of participants homes (Hoh et al., 2006).”
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.”
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 [ 13 , 18, 11] and in GPS traces [14].”
Quantifying Location Privacy: The Case of Sporadic Location Exposure (2011, background): “[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).”
A survey of computational location privacy (2009, methodology): “In their attacks on GPS data from vehicles, Hoh et al. [ 23 ] show that the success rate of the attacks generally goes down with increased temporal delay between location measurements, as one would expect.”
Virtual trip lines for distributed privacy-preserving traffic monitoring (2008, methodology): “Two recent studies [44, 36] observe about 15 minutes as a median trip time.”
VPriv: Protecting Privacy in Location-Based Vehicular Services (2009, background): “As discussed in Section 2, a number of papers have demonstrated that in low-density regions it is possible to reconstruct paths with some accuracy from anonymized traces [33, 34, 35].”
Preserving privacy in gps traces via uncertainty-aware path cloaking (2007, background): “Not surprisingly, recent analyses of GPS traces [34, 28, 26] have shown that simply omitting obvious identifiers from a dataset does not guarantee anonymity.”
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 ”
Inference Attacks on Location Tracks (2007, methodology): “In addition, Hoh et al.[13] describe one other computational technique:”
SignalGuru: leveraging mobile phones for collaborative traffic signal schedule advisory (2011, neutral): “grassroots participatory sensing [16].”
Protecting location privacy: optimal strategy against localization attacks (2012, background): “For instance, an adversary can, to name but a few possibilities, infer users’ activities from the frequency of their visits to certain locations [19]; reidentify anonymous low-granularity location traces given the users’ mobility profiles [5]; or derive [13], and re-identify [11, 18] the home addres”