Location privacy models in mobile applications: conceptual view and research directions

Damiani, Maria Luisa

doi:10.1007/s10707-014-0205-7

Cited by 30 publications

(13 citation statements)

References 47 publications

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“…The definition of privacy can be made scenario-specific by adopting an appropriate privacy measure, e.g., K-anonymity, differential privacy [ 30 ].…”

Section: Evaluation Of Contribution Strategiesmentioning

confidence: 99%

Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures

Bennati

Dusparić

Shinde

et al. 2018

Sensors

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Provision of smart city services often relies on users contribution, e.g., of data, which can be costly for the users in terms of privacy. Privacy risks, as well as unfair distribution of benefits to the users, should be minimized as they undermine user participation, which is crucial for the success of smart city applications. This paper investigates privacy, fairness, and social welfare in smart city applications by means of computer simulations grounded on real-world data, i.e., smart meter readings and participatory sensing. We generalize the use of public good theory as a model for resource management in smart city applications, by proposing a design principle that is applicable across application scenarios, where provision of a service depends on user contributions. We verify its applicability by showing its implementation in two scenarios: smart grid and traffic congestion information system. Following this design principle, we evaluate different classes of algorithms for resource management, with respect to human-centered measures, i.e., privacy, fairness and social welfare, and identify algorithm-specific trade-offs that are scenario independent. These results could be of interest to smart city application designers to choose a suitable algorithm given a scenario-specific set of requirements, and to users to choose a service based on an algorithm that matches their privacy preferences.

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“…The definition of privacy can be made scenario-specific by adopting an appropriate privacy measure, e.g., K-anonymity, differential privacy [ 30 ].…”

Section: Evaluation Of Contribution Strategiesmentioning

confidence: 99%

Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures

Bennati

Dusparić

Shinde

et al. 2018

Sensors

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“…We recently observe a growing interest in data privacy, especially related to location-aware applications [133]. In this respect, the identification and analysis of existing and potential scenarios for proximity-based opportunistic information sharing between citizens and/or city services are vital for securing privacy in personal services.…”

Section: Future Research Directions At the Intersection Between Smartmentioning

confidence: 99%

Opening up Smart Cities: Citizen-Centric Challenges and Opportunities from GIScience

Degbelo

Granell

Trilles

et al. 2016

IJGI

131

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Abstract:The holy grail of smart cities is an integrated, sustainable approach to improve the efficiency of the city's operations and the quality of life of citizens. At the heart of this vision is the citizen, who is the primary beneficiary of smart city initiatives, either directly or indirectly. Despite the recent surge of research and smart cities initiatives in practice, there are still a number of challenges to overcome in realizing this vision. This position paper points out six citizen-related challenges: the engagement of citizens, the improvement of citizens' data literacy, the pairing of quantitative and qualitative data, the need for open standards, the development of personal services, and the development of persuasive interfaces. The article furthermore advocates the use of methods and techniques from GIScience to tackle these challenges, and presents the concept of an Open City Toolkit as a way of transferring insights and solutions from GIScience to smart cities.

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“…This section discusses research results closely related to our work. For a more general survey on location privacy in the context of location-based services and mobile applications we refer the reader to [Krumm 2009;Ghinita 2013;Damiani 2014].…”

Section: Related Workmentioning

confidence: 99%

Protecting Against Velocity-Based, Proximity-Based, and External Event Attacks in Location-Centric Social Networks

Ghinita

Damiani

Silvestri

et al. 2016

ACM Trans. Spatial Algorithms Syst.

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Mobile devices with positioning capabilities allow users to participate in novel and exciting location-based applications. For instance, users may track the whereabouts of their acquaintances in location-aware social networking applications (e.g., Foursquare). Furthermore, users can request information about landmarks in their proximity. Such scenarios require users to report their coordinates to other parties, which may not be fully trusted. Reporting precise locations may result in serious privacy violations, such as disclosure of lifestyle details, sexual orientation, and so forth. A typical approach to preserve location privacy is to generate a cloaking region (CR) that encloses the user position. However, if locations are continuously reported, an attacker can correlate CRs from multiple timestamps to accurately pinpoint the user position within a CR.\ud \ud In this work, we protect against a broad range of attacks that breach location privacy using knowledge about (1) maximum user velocity, (2) external events that may occur outside the process of self-reporting locations (e.g., social network posts tagged by peers), and (3) information about mutual proximity between users. Assume user u who reports two consecutive cloaked regions A and B. We consider two distinct protection scenarios: in the first case, the attacker does not have information about the sensitive locations on the map, and the objective is to ensure that u can reach some point in B from any point in A; in the second case, the attacker knows the placement of sensitive locations, and the objective is to ensure that u can reach any point in B from any point in A. We propose spatial and temporal cloaking transformations to preserve user privacy, and we show experimentally that privacy can be achieved without significant quality-of-service deterioration

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Location privacy models in mobile applications: conceptual view and research directions

Cited by 30 publications

References 47 publications

Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures

Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures

Opening up Smart Cities: Citizen-Centric Challenges and Opportunities from GIScience

Protecting Against Velocity-Based, Proximity-Based, and External Event Attacks in Location-Centric Social Networks

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