Predictive Resource Management for Wearable Computing

By Dushyanth Narayanan, Mahadev Satyanarayanan. 2003

1,630 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Information Systems

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Citations in the corpus

Optimal Geo-Indistinguishable Mechanisms for Location Privacy (2014, background): “Several approaches are based in the notion of k-anonymity [25, 26, 27], requiring that the attacker cannot identify a user from at least other k−1 different users.”
Combating Attacks and Abuse in Large Online Communities (2016, methodology): “Earlier studies apply location cloaking by adding noise to the GPS report s [90].”
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].”
Protection of Location Privacy using Dummies for Location-based Services (2005, background): “The precision Proceedings of the 21st International Conference on Data Engineering (ICDE ’05) 1084-4627/05 $20.00 © 2005 IEEE of the position data is the same scale as the regions.”
SpaceTwist: Managing the Trade-Offs Among Location Privacy, Query Performance, and Query Accuracy in Mobile Services (2008, background): “Thus, some work [3]-[5], [8] represents the cloaked region Q' by a K-anonymous [13] rectangle, which contains the query location q and at least K -1 other user locations.”
A Framework for Protecting Worker Location Privacy in Spatial Crowdsourcing (2014, background): “However, the SC-server may not be trusted, and disclosing individual locations has serious privacy implications [10, 22, 8, 4].”
Wireless Sensor Network Security: A Survey (2006, methodology): “Some researchers have provided some approach using the anonymity mechanisms, for example, Gruteser and Grunwald [ 22 ] analyze the feasibility of anonymizing location information for location-based services in an automotive telematics environment; Beresford and Stajano [6] independently evaluate ano”
Differentially Private Filtering (2012, background): “Privacy-preserving mechanisms for such location-based services are often based on ad-hoc temporal and spatial cloaking of the measurements [6], [35].”
Mercury: a wearable sensor network platform for high-fidelity motion analysis (2009, background): “Odyssey [35] supports adaptations to changing network bandwidth [35], energy [14, 26], and computational load [34].”
Achieving k-anonymity in privacy-aware location-based services (2014, background): “Most of the existing approaches [6], [7], [8], [4], [9] assume that the adversary has no side information [10], [11], such as user’s query probability related to location and time, and information related to the semantics of the query such as the gender and social status of the user, and then dummy ”
Pseudonym Schemes in Vehicular Networks: A Survey (2015, methodology): “In introducing k-anonymity for location privacy, Gruteser and Grunwald [64] use k to represent”
Unraveling an old cloak: k-anonymity for location privacy (2010, methodology): “For example, one of the threats considered in [6] is “restricted space identification”.”
Source-location privacy in energy-constrained sensor network routing (2004, background): “The privacy threats that exist for sensor networks may be categorized into two broad classes: content-oriented security/privacy threats, and contextual privacy threats.”
Preserving User Location Privacy in Mobile Data Management Infrastructures (2006, methodology): “define k-anonymity in the context of location cloaking [2], which is the method that we will study in this paper.”
Blind Evaluation of Nearest Neighbor Queries Using Space Transformation to Preserve Location Privacy (2007, background): “Several techniques based on cloaking and K-anonymity have been proposed in the literature to reduce the resolution of the user’s location information [1, 2, 4–6, 13, 14].”