Preserving privacy in gps traces via uncertainty-aware path cloaking

By Baik Hoh, Marco Gruteser, Hui Xiong, Ansaf Ibrahem Alrabady. Conference on Computer and Communications Security, 2007

388 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/a6bb7731d5f9e7c03b50cb691cd067596a572246

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Citations in the corpus

Real time queue length estimation for signalized intersections using travel times from mobile sensors (2011, background): “This helps address the privacy issue when mobile sensors are widely available and the public is more concerned with privacy (Krumm, 2008; Hoh et al., 2007).”
Technical Privacy Metrics (2015, methodology): “To avoid the maximum tracking time’s overestimation of privacy, the mean time to confusion measures the time during which the adversary’s uncertainty stays below a confusion threshold τ [61].”
Anonysense: privacy-aware people-centric sensing (2008, background): “There is a wealth of research focused on this problem, and our architecture can be extended to include techniques such as k-anonymity and spatio-temporal cloaking [35, 15, 14, 19, 17] to reduce the leakage of private data through reports.”
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] ).”
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.”
Optimal Geo-Indistinguishable Mechanisms for Location Privacy (2014, background): “Most of the approaches in the literature are based on perturbing the information reported to the LBS provider, so to prevent the disclosure of the user’s location [4, 5, 6, 7, 8, 9].”
VTrack: accurate, energy-aware road traffic delay estimation using mobile phones (2009, background): “In addition, some recent papers [13, 15] have developed tools to quantify the degree of mixing of cars on a road needed to assure anonymity (notably the “time to confusion” metric).”
Unraveling an old cloak: k-anonymity for location privacy (2010, background): “In this paper, we focus on analyzing k-anonymity for location privacy at the microscopic level, as it has already been proven ineffective at the macroscopic level [8, 17].”
Quantifying and protecting location privacy (2015, neutral): “A large body of research has focused on developing location-privacy protection mechanisms (LPPMs) that allow users to make use of LBSs and limit the amount of disclosed sensitive information [1, 5, 9, 11, 13, 14, 18].”
Virtual trip lines for distributed privacy-preserving traffic monitoring (2008, methodology): “We thus adopt the time-to-confusion [37] metric and its variant distance-toconfusion, which measures the time or distance over which tracking may be possible.”
A survey of computational location privacy (2009, background): “Hoh et al. [ 28 ] go on to demonstrate an algorithm that selectively removes location samples with the goal of confusing an attacker.”
Predicting Users' Motivations behind Location Check-Ins and Utility Implications of Privacy Protection Mechanisms (2015, background): “Many location obfuscation mechanisms have been proposed, including reducing the granularity of the location (generalization), adding noise to the geographical location, adding fake location information, hiding location information, and changing identifiers [1], [4], [11], [15], [17].”
Protecting location privacy: optimal strategy against localization attacks (2012, background): “The most popular approach to obtaining location privacy is to send a space-or time-obfuscated version of the users’ actual locations to the service provider [10, 12, 14, 16].”