51,843 research outputs found
Privacy as a Public Good
Privacy is commonly studied as a private good: my personal data is mine to protect and control, and yours is yours. This conception of privacy misses an important component of the policy problem. An individual who is careless with data exposes not only extensive information about herself, but about others as well. The negative externalities imposed on nonconsenting outsiders by such carelessness can be productively studied in terms of welfare economics. If all relevant individuals maximize private benefit, and expect all other relevant individuals to do the same, neoclassical economic theory predicts that society will achieve a suboptimal level of privacy. This prediction holds even if all individuals cherish privacy with the same intensity. As the theoretical literature would have it, the struggle for privacy is destined to become a tragedy.
But according to the experimental public-goods literature, there is hope. Like in real life, people in experiments cooperate in groups at rates well above those predicted by neoclassical theory. Groups can be aided in their struggle to produce public goods by institutions, such as communication, framing, or sanction. With these institutions, communities can manage public goods without heavy-handed government intervention. Legal scholarship has not fully engaged this problem in these terms. In this Article, we explain why privacy has aspects of a public good, and we draw lessons from both the theoretical and the empirical literature on public goods to inform the policy discourse on privacy
Big Data Privacy Context: Literature Effects On Secure Informational Assets
This article's objective is the identification of research opportunities in
the current big data privacy domain, evaluating literature effects on secure
informational assets. Until now, no study has analyzed such relation. Its
results can foster science, technologies and businesses. To achieve these
objectives, a big data privacy Systematic Literature Review (SLR) is performed
on the main scientific peer reviewed journals in Scopus database. Bibliometrics
and text mining analysis complement the SLR. This study provides support to big
data privacy researchers on: most and least researched themes, research
novelty, most cited works and authors, themes evolution through time and many
others. In addition, TOPSIS and VIKOR ranks were developed to evaluate
literature effects versus informational assets indicators. Secure Internet
Servers (SIS) was chosen as decision criteria. Results show that big data
privacy literature is strongly focused on computational aspects. However,
individuals, societies, organizations and governments face a technological
change that has just started to be investigated, with growing concerns on law
and regulation aspects. TOPSIS and VIKOR Ranks differed in several positions
and the only consistent country between literature and SIS adoption is the
United States. Countries in the lowest ranking positions represent future
research opportunities.Comment: 21 pages, 9 figure
Averting Robot Eyes
Home robots will cause privacy harms. At the same time, they can provide beneficial services—as long as consumers trust them. This Essay evaluates potential technological solutions that could help home robots keep their promises, avert their eyes, and otherwise mitigate privacy harms. Our goals are to inform regulators of robot-related privacy harms and the available technological tools for mitigating them, and to spur technologists to employ existing tools and develop new ones by articulating principles for avoiding privacy harms.
We posit that home robots will raise privacy problems of three basic types: (1) data privacy problems; (2) boundary management problems; and (3) social/relational problems. Technological design can ward off, if not fully prevent, a number of these harms. We propose five principles for home robots and privacy design: data minimization, purpose specifications, use limitations, honest anthropomorphism, and dynamic feedback and participation. We review current research into privacy-sensitive robotics, evaluating what technological solutions are feasible and where the harder problems lie. We close by contemplating legal frameworks that might encourage the implementation of such design, while also recognizing the potential costs of regulation at these early stages of the technology
Why do People Adopt, or Reject, Smartphone Security Tools?
A large variety of security tools exist for Smartphones, to help their owners to secure the
phones and prevent unauthorised others from accessing their data and services. These range
from screen locks to antivirus software to password managers. Yet many Smartphone owners
do not use these tools despite their being free and easy to use. We were interested in exploring
this apparent anomaly. A number of researchers have applied existing models of behaviour
from other disciplines to try to understand these kinds of behaviours in a security context, and
a great deal of research has examined adoption of screen locking mechanisms. We review the
proposed models and consider how they might fail to describe adoption behaviours. We then
present the Integrated Model of Behaviour Prediction (IMBP), a richer model than the ones
tested thus far. We consider the kinds of factors that could be incorporated into this model in
order to understand Smartphone owner adoption, or rejection, of security tools. The model
seems promising, based on existing literature, and we plan to test its efficacy in future studies
Online privacy: towards informational self-determination on the internet : report from Dagstuhl Perspectives Workshop 11061
The Dagstuhl Perspectives Workshop "Online Privacy: Towards Informational Self-Determination on the Internet" (11061) has been held in February 6-11, 2011 at Schloss Dagstuhl. 30 participants from academia, public sector, and industry have identified the current status-of-the-art of and challenges for online privacy as well as derived recommendations for improving online privacy. Whereas the Dagstuhl Manifesto of this workshop concludes the results of the working groups and panel discussions, this article presents the talks of this workshop by their abstracts
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Big Data Assurance Evaluation: An SLA-Based Approach.
The Big Data community has started noticing that there is the need to complete Big Data platforms with assurance techniques proving the correct behavior of Big Data
analytics and management. In this paper, we propose a Big Data assurance solution based on Service-Level Agreements (SLAs), focusing on a platform providing Model-based Big Data Analytics-as-a-Service (MBDAaaS)
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