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Multilaterally Secure Pervasive Cooperation
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The present paper summarizes the PhD thesis of Stefan G. Weber.
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... However, the use of transaction pseudonyms can undermine the utility of provenance graphs, which requires a certain degree of linkability. The concept of multi-level linkable transaction pseudonyms [28] could be applied in 4 A view refers to the result set of a database query. ...
Provenance data can be expressed as a graph with links informing who and which activities created, used and modified entities. The semantics of these links and domain specific reasoning can support the inference of additional information about the elements in the graph. If such elements include personal identifiers and/or personal identifiable information, then inferences may reveal unexpected links between elements, thus exposing personal data beyond an individual's intentions. Provenance graphs often entangle data relating to multiple individuals. It is therefore a challenge to protect personal data from unintended disclosure in provenance graphs. In this paper, we provide a Privacy Impact Assessment (PIA) template for identifying imminent privacy threats that arise from provenance graphs in an application-agnostic setting. The PIA template identifies privacy threats, lists potential countermeasures, helps to manage personal data protection risks, and maintains compliance with privacy data protection laws and regulations.
... The comprehensive concepts for the representation of digital identities as well as an overview of main principles of this work were presented in [29]. The PhD thesis is summarized in [22]. Furthermore, a cooperation lead to an additional publication [7], addressing issues of multilateral security and principal limits of privacy protection as well as of transparency and audit mechanisms in a related e-government services context 1 . ...
The historic development of computing can be broadly described by three historic waves: (1.)
the ‘many persons, one computer’ era, (2.) the ‘one person, one computer’ era, and (3.) the
‘one person, many computers’ era.
The first wave (starting in the 1950s) is aptly termed the ‘many persons, one computer’ era.
The one computer, coming in the form of a mainframe or minicomputer, was mostly used by
specialists and deployed in industrial environments to reliably handle large scale data
processing tasks.
The second wave of computing set in the late 1970s, the ‘one person, one computer’ era, is
characterized by every employee or private person owning or using a computer, either for
professional purposes or for leisure. By now, some industries (such as banking) see over 95%
of their employees1 working on computer terminals and 87% of German households2 owned a
PC in 2006. Thus, this second wave of computing is reaching saturation in recent years, at
least in the industrialised part of the world.
The third wave of computing, which can be said to have started in the mid 1990s, is called the
‘one person, many computers’ era. It is characterized by computer chips increasingly being
embedded in a vast array of consumer devices, such as smart phones, digital cameras, toys,
cars, etc. The end-vision of this computing era is what some scholars have termed ‘Ubiquitous
Computing’.
Ubiquitous Computing (hereafter often abbreviated as ‘UC’) refers to environments where
most physical objects are enhanced with digital qualities. It is technically based on two
building blocks: embedded computing and mobile communications (Lyytinen and Yoo 2002).
Embedded computing implies that just about any kind of every day object, as well as the
natural environment, human beings and animals, are infused with computing capabilities.
Active and passive Radio Frequency Identification (RFID) tags, sensors, video cameras and
the fusion of information stemming from these diverse systems are on the verge of leading to
a ‘naturally’ computerized environment, while mobile wireless communication technologies
such as RFID, Bluetooth or Wireless-LANs are used to hook up to these distributed
computing devices and ‘capture and access’ information from them for aggregation,
integration and service creation at the backend.
This is the first self-contained text to consider security and non-cooperative behavior in wireless networks. Major networking trends are analyzed and their implications explained in terms of security and cooperation, and potential malicious and selfish misdeeds are described along with the existing and future security techniques. Fundamental questions of security including user and device identification; establishment of security association; secure and cooperative routing in multi-hop networks; fair bandwidth distribution; and privacy protection are approached from a theoretical perspective and supported by real-world examples including ad hoc, mesh, vehicular, sensor, and RFID networks. Important relationships between trust, security, and cooperation are also discussed. Contains homework problems and tutorials on cryptography and game theory. This text is suitable for advanced undergraduates and graduate students of electrical engineering and computer science, and researchers and practitioners in the wireless industry. Lecture slides and instructor-only solutions available online (www.cambridge.org/9780521873710).
Privacy is a socially constructed value that differs significantly across environments and age cohorts of individuals. The impact of ubiquitous computing (ubicomp) on privacy will be the most intense in home-based healthcare. Value-sensitive design has the potential to make this transformational change less disruptive in terms of personal autonomy and individual boundaries by integrating privacy into ubicomp home healthcare. Yet value-sensitive design must be predicated upon a shared concept of the particular value under consideration.
Secret sharing allows a secret key to be distributed among n persons, such that k(1 <= k <= n) of these must be present in order to recover it at a later time. This report first shows how this can be done such that every person can verify (by himself) that his part of the secret is correct even though fewer than k persons get no Shannon information about the secret. However, this high level of security is not needed in public key schemes, where the secret key is uniquely determined by a corresponding public key. It is therefore shown how such a secret key (which can be used to sign messages or decipher cipher texts) can be distributed. This scheme has the property, that even though everybody can verify his own part, sets of fewer than k persons cannot sign/decipher unless they could have done so given just the public key. This scheme has the additional property that more than k persons can use the key without compromising their parts of it. Hence, the key can be reused. This technique is further developed to be applied to undeniable signatures. These signatures differ from traditional signatures as they can only be verified with the signer's assistance. The report shows how the signer can authorize agents who can help verifying signatures, but they cannot sign (unless the signer permits it).
The present chapter is intended as a lightweight introduction to ubiquitous computing as a whole, in preparation for the more specific book parts and chapters that cover selected aspects. This chapter thus assumes the preface of this book to be prior knowledge. In the following, a brief history of ubiquitous computing (UC) is given first, concentrating on selected facts considered as necessary background for understanding the rest of the book. Some terms and a few important standards are subsequently mentioned that are considered necessary for understanding related literature. For traditional standards like those widespread in the computer networks world, at least superficial knowledge must be assumed since their coverage is impractical for a field with such diverse roots as UC. In the last part of this chapter, we will discuss two kinds of reference architectures, explain why they are important for the furthering of Ubiquitous Computing and for the reader’s understanding, and briefly sketch a few of these architectures by way of example.
To support users in performing their tasks, applications need a better understanding of the current situation they are being used in. This chapter gives an overview of how knowledge of the current context, that is, information characterizing the situation, can be represented and how this knowledge can be used for enhancing applications. We discuss what is actually meant by “context” and “context-aware” applications. Further, we describe what has to be considered when building a context-aware application. We thereby focus on the representation of context information and how to deal with its unreliable nature. This chapter should sensitize the reader to the difficulties of using context information and give guidelines on how to build an application that benefits from knowing its current context.