VPriv: Protecting Privacy in Location-Based Vehicular Services

By Raluca Ada Popa, Hari Balakrishnan, Andrew Justin Blumberg. 2009

154 citations recorded by the source corpus.

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Citations in the corpus

Prio: Private, Robust, and Scalable Computation of Aggregate Statistics (2017, background): “The servers can sum up the encrypted bits and decrypt only the final sum [48, 56, 81, 92, 99, 100].”
Verifying computations with state (2013, methodology): “This example is inspired by [62], which requires a custom protocol, while we require only a simple C program (§7).”
Privacy and accountability for location-based aggregate statistics (2011, background): “VPriv [32] and PrETP [2] compute functions over a particular driver’s path as opposed to computing statistics over the paths of multiple clients; for instance, they can compute the total toll that driver Alice has to pay based on how much she drove in a month.”
Location Privacy via Private Proximity Testing (2011, background): “Another line of work aims to mitigate the privacy issues in tracking and monitoring of vehicles; see for example [35].”
Private memoirs of a smart meter (2010, background): “However, they have been successfully applied to provide location privacy to vehicular services [22, 3].”
The Internet of Things: A Survey from the Data-Centric Perspective (2013, unknown): “Examples of systems which use aggregate data for privacy-preserving queries in smart vehicular sensing environments are discussed in [87].”
Measuring Large-Scale Social Networks with High Resolution (2014, methodology): “Homomorphic encryption, on the other hand, can be used to perform computation directly on the encrypted data, thus eliminating the need of exposing any sensitive information [91–94]; this technique has been applied, for example, to vehicle positioning data [95] and medical records [96].”