Location privacy in pervasive computing

By Claudio Agostino Ardagna, Marco Cremonini, Sabrina De Capitani di Vimercati, Pierangela Samarati. 2008

965 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b0b46a049ce01a88c75fb81b45b99b583140623d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

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].”
It's the way you check-in: identifying users in location-based social networks (2014, background): “However, a great deal of research has highlighted the dangers of exposing personal location information [2, 5, 20].”
A survey of computational location privacy (2009, methodology): “Entropy is the privacy quantifier used by Beresford and Stajano [6].”
Location Privacy via Private Proximity Testing (2011, background): “Another seminal work is by Beresford and Stajano who introduced “mix zones” [4].”
Wireless Sensor Network Security: A Survey (2006, methodology): “All aspects of the wireless sensor network are being examined including secure and efficient routing [12], [37], [57], [74], data aggregation [19], [29], [48], [63], [70], [84], group formation [6], [38], [64], and so on.”
Security in wireless sensor networks: issues and challenges (2006, background): “…environmental monitoring etc. Today’s sensors can monitor temperature, pressure, humidity, soil makeup, vehicular movement, noise levels, lighting conditions, the presence or absence of certain kinds of objects or substances, mechanical stress levels on attached objects, and other properties [4].”
Preventing Location-Based Identity Inference in Anonymous Spatial Queries (2007, methodology): “Section 2.1 discusses K -anonymity in relational databases, and Section 2.2 presents privacy-preserving methods for LBSs. Section 2.3 overviews related spatial query processing techniques.”
Supporting anonymous location queries in mobile environments with privacygrid (2008, methodology): “Before reporting our experimental results, we .rst describe the experimental setup, including the road-network based mobile object simulator used in the experiments.”
The new Casper: query processing for location services without compromising privacy (2006, methodology): “Motivated by the privacy threats of location-detection devices [1, 6, 8, 44], several research eﬀorts are dedicated to protect the user location privacy (e.g., false dummies [27], landmark objects [21], and location perturbation [13, 16, 17]).”
Optimal Geo-Indistinguishable Mechanisms for Location Privacy (2014, background): “Others are based on the idea of letting the users use pseudonyms to interact with the system, and on having regions (mix zones, [4, 6]), where the users can change their pseudonyms without being traced by the system.”
Protecting location privacy: optimal strategy against localization attacks (2012, background): “A different approach consists in hiding some of the users’ locations by using mix zones [1, 9], or silent periods [15].”
On the Anonymity of Home/Work Location Pairs (2009, background): “Intentional degradation of location information quality, or obfuscation, is a wellknown technique for preserving the anonymity, or pseudonymity, of location traces [2, 3], but the question of how much obfuscation is required to preserve anonymity is often sidestepped.”
Privacy in the Internet of Things: threats and challenges (2014, background): “Extensive privacy research has been conducted focussing mostly on location-based services [52, 53, 54], detection of privacy breaches [55, 56] and privacy aware architectures for participatory sensing [57, 58].”
A survey of indoor positioning systems for wireless personal networks (2009, background): “Controlling access to the location information [29] and distribution of the information [11] can improve the privacy in IPSs.”
Preserving User Location Privacy in Mobile Data Management Infrastructures (2006, background): “According to [4], LBS applications can be non-anonymous, pseudonymous and anonymous.”
Quantifying and protecting location privacy (2015, unknown): “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].”
Quantifying Location Privacy: The Case of Sporadic Location Exposure (2011, background): “Golle and Partridge [7], Beresford and Stajano [2], Hoh et al.”
A survey on privacy in mobile participatory sensing applications (2011, background): “The particular interest for location privacy may be due to the sharing of similar concerns with orthogonal domains addressing this issue, such as vehicular networks [59], location-based services [60], pervasive computing [61], ubiquitous computing [62], etc.”