Protecting Location Privacy Through Path Confusion

By Baik Hoh, Marco Gruteser. Security and Privacy in Communication Networks, 2005

463 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications

View Paper: https://www.semanticscholar.org/paper/6f4a0e11ee9cd1745a98f9995f3d04b32f88454c

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Citations in the corpus

Pseudonym Schemes in Vehicular Networks: A Survey (2015, background): “For instance, Hoh and Gruteser [62] quantify location pri-”
Broadening the Scope of Differential Privacy Using Metrics (2013, background): “Some of these works [19,20,21] propose the use of the expectation of distance error of the attacker as the way to quantify the privacy offered by a mechanism.”
Virtual trip lines for distributed privacy-preserving traffic monitoring (2008, background): “There are many best effort approaches [14, 54, 45, 35, 15] that degrade information in a controlled way before releasing it.”
Achieving k-anonymity in privacy-aware location-based services (2014, background): “Some other metrics [24], [25] are based on the estimation error of the adversary to quantify the location privacy.”
Privacy Preservation in the Publication of Trajectories (2008, background): “A problem related to sequence anonymization has been addressed in [9].”
Preventing Location-Based Identity Inference in Anonymous Spatial Queries (2007, methodology): “Hoh and Gruteser [16] describe techniques for hiding the trajectory of users in applications that continuously collect location samples.”
Preserving privacy in gps traces via uncertainty-aware path cloaking (2007, background): “Other techniques [6, 27, 36] achieve better accuracy but cannot guarantee privacy in low user density scenarios.”
Location privacy in pervasive computing (2008, background): “Finally, another strand of research is aimed at protecting the path privacy of the users [23] [25] [30].”
Mix-Zones for Location Privacy in Vehicular Networks (2007, background): “[12] M. Gruteser and D. Grunwald.”
Technical Privacy Metrics (2015, background): “to the adversary’s expected estimation error, the expectation of distance error measures the expected distance error of an adversary, but over multiple timesteps T and location assignment hypotheses H[66]. Each hypothesis h assigns a user to a location with probability p h,t(x), and the distance d h”
Geo-indistinguishability: differential privacy for location-based systems (2012, methodology): “In [23], a perturbation mechanism is used to confuse the attacker by crossing paths of individual users, rendering the task of tracking individual paths challenging.”
Unraveling an old cloak: k-anonymity for location privacy (2010, background): “, location perturbation [7], time perturbation [11]); or they may add some dummy queries that are indistinguishable from the real queries [3].”
Quantifying Location Privacy: The Case of Sporadic Location Exposure (2011, background): “In this paper, we focus on the sporadic case (for some examples of the continuous case see [9,11] ).”