Location Privacy for Mobile Crowd Sensing through Population Mapping

Opportunistic sensing allows applications to “task” mobile devices to measure context in a target region. For example, one could leverage sensor-equipped vehicles to measure traffic or pollution levels on a particular street or users\u27 mobile phones to locate (Bluetooth-enabled) objects in their vicinity. In most proposed applications, context reports include the time and location of the event, putting the privacy of users at increased risk: even if identifying information has been removed from a report, the accompanying time and location can reveal sufficient information to de-anonymize the user whose device sent the report. We propose and evaluate a novel spatiotemporal blurring mechanism based on tessellation and clustering to protect users\u27 privacy against the system while reporting context. Our technique employs a notion of probabilistic k-anonymity; it allows users to perform local blurring of reports efficiently without an online anonymization server before the data are sent to the system. The proposed scheme can control the degree of certainty in location privacy and the quality of reports through a system parameter. We outline the architecture and security properties of our approach and evaluate our tessellation and clustering algorithm against real mobility traces

(So) Big Data and the transformation of the city

The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality

Spatial crowdsourcing with mobile agents in vehicular networks

In the last years, the automotive industry has shown interest in the addition of computing and communication devices to cars, thanks to technological advances in these fields, in order to meet the increasing demand of “connected” applications and services. Although vehicular ad hoc networks (VANETs) have not been fully developed yet, they could be used in a near future as a means to provide a number of interesting applications and services that need the exchange of data among vehicles and other data sources. In this paper, we propose a spatial crowdsourcing schema for the opportunistic collection of information within an interest area in a city or region (e.g., measures about the environment, such as the concentration of certain gases in the atmosphere, or information such as the availability of parking spaces in an area), using vehicular ad hoc communications. We present a method that exploits mobile agent technology to accomplish the distributed collection and querying of data among vehicles in such a scenario. Our proposal is supported by an extensive set of realistic simulations that prove the feasibility of the approach

Tactful Networking: Humans in the Communication Loop

International audienceThis survey discusses the human-perspective into networking through the Tactful Networking paradigm, whose goal is to add perceptive senses to the network by assigning it with human-like capabilities of observation, interpretation, and reaction to daily-life features and associated entities. To achieve this, knowledge extracted from inherent human behavior in terms of routines, personality, interactions, and others is leveraged, empowering the learning and prediction of user needs to improve QoE and system performance while respecting privacy and fostering new applications and services. Tactful Networking groups solutions from literature and innovative interdisciplinary human aspects studied in other areas. The paradigm is motivated by mobile devices' pervasiveness and increasing presence as a sensor in our daily social activities. With the human element in the foreground, it is essential: (i) to center big data analytics around individuals; (ii) to create suitable incentive mechanisms for user participation; (iii) to design and evaluate both humanaware and system-aware networking solutions; and (iv) to apply prior and innovative techniques to deal with human-behavior sensing and learning. This survey reviews the human aspect in networking solutions through over a decade, followed by discussing the tactful networking impact through literature in behavior analysis and representative examples. This paper also discusses a framework comprising data management, analytics, and privacy for enhancing human raw-data to assist Tactful Networking solutions. Finally, challenges and opportunities for future research are presented