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Spatio-temporal, geo-referenced datasets are growing rapidly, and will be more in the near future. This phenomenon is mostly due to the daily collection of telecommunication data from mobile phones and other location-aware devices and is expected to enable novel classes of applications based on the extraction of behavioral patterns from mobility data. Such patterns could be used for instance in traffic and sustainable mobility management (e.g., to study the accessibility to services), urban planning, environmental monitoring, and collaborative location-based services. Clearly, in these applications privacy is a concern, since some knowledge may be sensitive, or an over-specific pattern may reveal the behaviour of groups of few individual. In this paper we focus on automated privacy-preserving methods we developed for extracting and sharing user-consumable forms of knowledge from large amounts of raw data referenced in space and in time. ©2007 IEEE., IEEE Computer Society;Informatik-Forum Stuttgart;NEC Europe Ltd. Network Laboratories

Spatio-temporal traces left behind by moving individuals are increasingly available. On the one hand, mining this kind of data is expected to produce interesting behavioral knowledge enabling novel classes of mobility applications; but on the other hand, due to the peculiar nature of position data, mining it creates important privacy concerns. Thus, studying privacy preserving data mining methods for moving object data is interesting and challenging. In this paper, we address the problem of hiding sensitive trajectory patterns from moving objects databases. The aim is to modify the database such that a given set of sensitive trajectory patterns can no longer be extracted, while the others are preserved as much as possible. We provide the formal problem statement and show that it is NP-hard; so we devise heuristics and a polynomial sanitization algorithm. We discuss a possible attack to our model, that exploits the knowledge of the underlying road network, and we enhance our model to protect from this kind of attacks. Experimental results show the effectiveness of our proposal. © 2007 IEEE.

Spatio-temporal, geo-referenced datasets are growing rapidly, and will be more in the near future. This phenomenon is mostly due to the daily collection of telecommunication data from mobile phones and other location-aware devices and is expected to enable novel classes of applications based on the extraction of behavioral patterns from mobility data. Such patterns could be used for instance in traffic and sustainable mobility management (e.g., to study the accessibility to services), urban planning, environmental monitoring, and collaborative location-based services. Clearly, in these applications privacy is a concern, since some knowledge may be sensitive, or an over-specific pattern may reveal the behaviour of groups of few individual. In this paper we focus on automated privacy-preserving methods we developed for extracting and sharing user-consumable forms of knowledge from large amounts of raw data referenced in space and in time. ©2007 IEEE., IEEE Computer Society;Informatik-Forum Stuttgart;NEC Europe Ltd. Network Laboratories

Spatio-temporal traces left behind by moving individuals are increasingly available. On the one hand, mining this kind of data is expected to produce interesting behavioral knowledge enabling novel classes of mobility applications; but on the other hand, due to the peculiar nature of position data, mining it creates important privacy concerns. Thus, studying privacy preserving data mining methods for moving object data is interesting and challenging. In this paper, we address the problem of hiding sensitive trajectory patterns from moving objects databases. The aim is to modify the database such that a given set of sensitive trajectory patterns can no longer be extracted, while the others are preserved as much as possible. We provide the formal problem statement and show that it is NP-hard; so we devise heuristics and a polynomial sanitization algorithm. We discuss a possible attack to our model, that exploits the knowledge of the underlying road network, and we enhance our model to protect from this kind of attacks. Experimental results show the effectiveness of our proposal. © 2007 IEEE.

The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the “phase 2” of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens’ privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens’ “personal data stores”, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates—if an

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., QC 20201202AIP

Social media, and in particular microblogging sites, allow users to post multiple kinds of content for different purposes. Content may be purely conversational, or news-related, or event-related. To find information relevant to users in this heterogeneous mass of content, it would be important to consider the task for which search is carried out, and the most suitable relevance dimensions. In the last years, despite the social search problem has been increasingly investigated, this aspect has not been sufficiently analyzed. For this reason, in this paper, we focus on different search tasks in the microblog search context, and we identify some related relevance dimensions. We also report some experiments we have made to verify the impact of the identified relevance dimensions on the system effectiveness, with respect to the considered search tasks.

© 2020, University of Skovde. All rights reserved. The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the “phase 2” of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens’ privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens’ “personal data stores”, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: It allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow