Virtual trip lines for distributed privacy-preserving traffic monitoring

By Baik Hoh, Marco Gruteser, Ryan Herring, Jeff Ban, D. Work, Juan Carlos Herrera, Alexandre M. Bayen, Murali Annavaram, Quinn Jacobson. ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, 2008

420 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Building and Construction, Transportation

View Paper: https://www.semanticscholar.org/paper/9ca2046924054028a7247f560c5f232a68e62695

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Citations in the corpus

ParkNet: drive-by sensing of road-side parking statistics (2010, methodology): “[3] uses the concept of virtual triplines to address the privacy problems in traffic monitoring systems”
The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data (2011, positive): “This concept was popularized by Nokia under the term Virtual Trip Line [19].”
VTrack: accurate, energy-aware road traffic delay estimation using mobile phones (2009, methodology): “The virtual triplines scheme [13] proposes a way to determine when it is “safe” from the standpoint of privacy for a vehicle to report its position using such a quantification.”
A framework of energy efficient mobile sensing for automatic user state recognition (2009, background): “sensing and context information [1] have become primary inputs for a new class of mobile cooperative services such as real time traffic monitoring [2], and social networking applications such as Facebook [3] and MySpace [4].”
Improving energy efficiency of location sensing on smartphones (2010, background): “Some instances of current ly popular LBAs include mobile social networking [2, 3, 9, 10], hea lthcare [1], local traffic [7,22,23,29,36], and local restaura nts [6].”
A traffic model for velocity data assimilation (2010, background): “With the cellular phone communication infrastructure in place and privacy aware smartphone sensing technology in full expansion [17], a large volume of data from mobile devices is now available [16].”
Urban traffic signal control with connected and automated vehicles: A survey (2019, methodology): “Recently developed concepts of virtual trip lines (VTL; see Hoh et al. (2008)) and VTL-zones (Sun et al., 2013), and methods such as privacy by design to “co-design” privacy techniques and modeling methods (Cavoukian, 2009; Ban and Gruteser, 2010), are some examples on how privacy may be…”
An ensemble Kalman filtering approach to highway traffic estimation using GPS enabled mobile devices (2008, methodology): “IMPLEMENTATION AND VALIDATION We have implemented the EnKF on the LWR-v PDE as a traffic estimation algorithm in a privacy-preserving architecture described in detail in earlier work [37].”
A Survey of Physics-Based Attack Detection in Cyber-Physical Systems (2018, background): “There is also ongoing research on CPS privacy including smart grids [43], vehicular location monitoring [37], and location privacy [91].”
Analytical and grid-free solutions to the Lighthill-Whitham-Richards traffic flow model (2011, background): “For instance, most traffic estimation applications require high resolution output such as δx ≤ 200m, see for example [22, 36, 23, 31].”
Real time queue length estimation for signalized intersections using travel times from mobile sensors (2011, background): “It turns out that collecting intersection travel time or delay samples instead of detailed vehicle trajectories can also lead to improved privacy protection, if the data collection and sampling schemes are properly designed (Hoh et al., 2008).”
PRISM: platform for remote sensing using smartphones (2010, background): “Finally, privacy can be further strengthened by adopting cloaking techniques presented in [23, 24] such that registrations from any given region are sent to PRISM servers only”
P3: Toward Privacy-Preserving Photo Sharing (2013, neutral): “Tangentially related is a body of work in the computer systems community on ensuring other forms of privacy: secure distributed storage systems [23, 37, 11], and privacy and anonymity for mobile systems [19, 26, 16].”
VPriv: Protecting Privacy in Location-Based Vehicular Services (2009, background): “This protocol, which belongs to the general family of secure multi-party computations, guarantees that a joint computation between server and client can proceed correctly without revealing the private data of the parties involved.”
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.”
SignalGuru: leveraging mobile phones for collaborative traffic signal schedule advisory (2011, background): “Several systems have been proposed that leverage GPS, accelerometer and proximity sensors in order to estimate traffic conditions [15, 23, 27], detect road abnormalities [11], collect information for available parking spots [22] and compute fuel efficient routes [12].”
Pseudonym Schemes in Vehicular Networks: A Survey (2015, background): “, the distance-toconfusion [187]) and can act upon it by deciding whether and when to change its pseudonym.”