Identity, profiling algorithms and a world of ambient intelligence (original) (raw)
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Future of Identity in the Information Society (FIDIS), 2007
Both corporate and global governance seem to demand increasingly sophisticated means for identification. Against a justification in terms of dealing with security threats, fraud and abuse, citizens are screened, located, detected and their data stored, aggregated and analysed. At the same time potential customers are profiled to detect their habits and preferences in order to provide for targeted services. Both industry and the European Commission are investing huge sums of money into what they call Ambient Intelligence and the creation of an Internet of Things. Such intelligent networked environments will entirely depend on real time monitoring and real time profiling, resulting in real time adaptation of the environment. In this contribution the author assesses the threats and opportunities of such autonomic profiling in terms of its impact on individual autonomy and refined discrimination and indicates the extent to which traditional data protection is ineffective as regards profiling.
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Deliverables 7.14a and 7.14b seek to detect in which way the new type of profiling that is the subject of FIDIS work package 7 – machine profiling based on data mining techniques – is different from previous ways of profiling, and how this relates to the construction of our identity. The concepts of idem and ipse, coined by the French philosopher Ricoeur, are used to look into the issue of human identity as something that emerges between a person and her environment. This report, D7.14b, discusses how the concepts explored in D7.14a apply in concrete contexts of profiling. It describes four contexts, ranging from present-day to future ICT-related practices: 1) ICT-based identification in business-consumer and government-citizen relationships, with a focus on trust-enhancing tools; 2) the social web (blogs, wikis, social networking); 3) virtual worlds (gaming and persistent worlds); and 4) a cyborg interacting with his environment. Based on the profiling practices occurring in these ...
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Datenschutz und Datensicherheit-DuD, 2006
Profiling is not about data but about knowledge. It provides a crucial technology in a society that is flooded with noise and information. Profiling is another term for sophisticated pattern recognition, and the enabling technology for Ambient Intelligence. It confronts us with a new type of inductive knowledge, inferred by means of automated algorithms. To the extent that decisions that impact our lives are based on such knowledge, we need to develop the means to make this knowledge accessible for individual citizens and provide them with the legal and technological tools to anticipate and contest such knowledge or challenge its application. Dr Mireille Hildebrandt Senior Researcher at LSTS, Vrije Universiteit Brussel, teaching at Faculty of Law Erasmus University Rotterdam.
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The success of Ambient Intelligence (AmI) will depend on how secure it can be made, how privacy and other rights of individuals can be protected and how individuals can come to trust the intelligent world that surrounds them and through which they move. This contribution presents an analysis of ambient intelligence scenarios, particularly in regard to AmI's impacts on and implications for individual privacy. The analysis draws on our review of more than 70 AmI projects, principally in Europe. It notes the visions as well as the specifics of typical AmI scenarios. Several conclusions can be drawn from the analysis, not least of which is that most AmI scenarios depict a rather too sunny view of our technological future. Finally, reference is made to the SWAMI project (Safeguards in a World of Ambient Intelligence) which, inter alia, has constructed "dark" scenarios, as we term them, to show how things can go wrong in AmI and where safeguards are needed.
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Telematics and Informatics, 2007
The success of Ambient Intelligence (AmI) will depend on how secure it can be made, how privacy and other rights of individuals can be protected and how individuals can come to trust the intelligent world that surrounds them and through which they move. This article addresses these issues by analysing scenarios for ambient intelligence applications that have been developed over the last few years. It elaborates the assumptions that promotors make about the likely use of the technology and possibly unwanted side effects. It concludes with a number of threats for personal privacy that become evident.
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This book is a warning. It aims to warn policy-makers, industry, academia, civil society organisations, the media and the public about the threats and vulnerabilities facing our privacy, identity, trust, security and inclusion in the rapidly approaching world of ambient intelligence (AmI). In the near future, every manufactured product – our clothes, money, appliances, the paint on our walls, the carpets on our floors, our cars, everything – will be embedded with intelligence, networks of tiny sensors and actuators, which some have termed "smart dust". The AmI world is not far off. We already have surveillance systems, biometrics, personal communicators, machine learning and more. AmI will provide personalised services – and know more about us – on a scale dwarfing anything hitherto available. In the AmI vision, ubiquitous computing, communications and interfaces converge and adapt to the user. AmI promises greater user-friendliness in an environment capable of recognising...
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For the past few decades people have faced a dramatic technological development. One of the new technologies that are predicted to be massively widespread in the future is Ambient Intelligence. One can imagine Ambient Intelligence as an environment with embedded sensors enabling the environment to anticipate wishes of its users and to adapt itself accordingly. Development and utilization of such environments, however, entail new threats. The aim of this paper is to identify these threats and the dangers resulting from the need to collect and further process all kinds of data in order to provide highly personalized services.