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Trusted IoT in ambient assisted living scenarios
Published in Abbas Moallem, Human-Computer Interaction and Cybersecurity Handbook, 2018
Elias Z. Tragos, Alexandros Fragkiadakis, Aqeel Kazmi, Martin Serrano
In the conceptual model for AHA-IoT ecosystem, the components for trust, security, and privacy are core components. Data streams are the core asset of the ecosystem, which belongs to either private or public sources. As in all AAL applications, private data are produced by wearable and medical devices as well as smart sensors and devices in older adults’ living environments. Public data, not necessarily linked to user interactions, are harnessed from public sources, including weather data, public transport timetables, and traffic situations. Both private and public data are processed at the edge and/or at cloud level. These data streams are then passed through different processes, such as anonymization, aggregation, and analysis that aim to increase the security and privacy of the overall system and thus its trustworthiness.
Access and Privilege in Big Data Analysis
Published in Kevin E. Foltz, William R. Simpson, Enterprise Level Security 2, 2020
Kevin E. Foltz, William R. Simpson
A lot of the work in data mining focuses on how to use big data sets to do new things. Much of the work uses data that is either publicly available or generated in house. In either case, ACRs are uniform across the data. Public data is available to anyone. In-house data is available to those owning the data. As a result, the problems focus on data quality, data heterogeneity, new applications for the data, combining different data sets, or other issues with the complete data sets.
Data to the people: a review of public and proprietary data for transport models
Published in Transport Reviews, 2021
Vishal Mahajan, Nico Kuehnel, Aikaterini Intzevidou, Guido Cantelmo, Rolf Moeckel, Constantinos Antoniou
The public and proprietary data are differentiated in Figure 1. Informally, the term “Public data5” refers to publicly available, free data with or without usage restrictions. In this paper, we formally define public data as a superset of open data, inspired by Kerle (2018) and Wynne-Jones (2019). When data are accessible, allowed to be used for any purpose and redistributed free of charge with almost no restrictions, they can be termed Open data (The World Bank, 2019). In this paper, the term public data refers to data that are accessible and free of cost. Unlike open data, public data can be restricted in their usage (e.g. non-commercial licensing) and shareability. Consequently, while open data are always “public”, public data are not necessarily “open”. Furthermore, public data are not the same as Public Sector Information (PSI), where the latter denotes data emerging from government institutions. During the past few years, the data revolution has played a definitive role in creating public awareness and participation in the use of public data. The number of published articles shows that research using public data has gained momentum in the last 15 years (Figure 2). This rise in public or open data research was strengthened by policy initiatives introduced in 2009/2010 to increase access to government data. The open data revolution received a significant push by Obama’s Open Government Directive in 2009 (US Government, 2009) to increase transparency in the Executive branch. This step was complemented by other initiatives, such as the Open Government Partnership (OGP, 2011) initiative, the amendment to the EU’s PSI Directive in 2013 (European Commission [EC], 2013) or the G8 Open Data Charter in 2013 (Welle Donker & van Loenen, 2017). These and many other initiatives in different parts of the world continue to advance the formalisation of open data's legal and technical aspects (Janssen, 2011).