37,676 research outputs found
On-line privacy behavior: using user interfaces for salient factors
The problem of privacy in social networks is well documented within literature; users have privacy concerns however, they consistently disclose their sensitive information and leave it open to unintended third parties. While numerous causes of poor behaviour have been suggested by research the role of the User Interface (UI) and the system itself is underexplored. The field of Persuasive Technology would suggest that Social Network Systems persuade users to deviate from their normal or habitual behaviour. This paper makes the case that the UI can be used as the basis for user empowerment by informing them of their privacy at the point of interaction and reminding them of their privacy needs. The Theory of Planned Behaviour is introduced as a potential theoretical foundation for exploring the psychology behind privacy behaviour as it describes the salient factors that influence intention and action. Based on these factors of personal attitude, subjective norms and perceived control, a series of UIs are presented and implemented in controlled experiments examining their effect on personal information disclosure. This is combined with observations and interviews with the participants. Results from this initial, pilot experiment suggest groups with privacy salient information embedded exhibit less disclosure than the control group. This work reviews this approach as a method for exploring privacy behaviour and proposes further work required
ConXsense - Automated Context Classification for Context-Aware Access Control
We present ConXsense, the first framework for context-aware access control on
mobile devices based on context classification. Previous context-aware access
control systems often require users to laboriously specify detailed policies or
they rely on pre-defined policies not adequately reflecting the true
preferences of users. We present the design and implementation of a
context-aware framework that uses a probabilistic approach to overcome these
deficiencies. The framework utilizes context sensing and machine learning to
automatically classify contexts according to their security and privacy-related
properties. We apply the framework to two important smartphone-related use
cases: protection against device misuse using a dynamic device lock and
protection against sensory malware. We ground our analysis on a sociological
survey examining the perceptions and concerns of users related to contextual
smartphone security and analyze the effectiveness of our approach with
real-world context data. We also demonstrate the integration of our framework
with the FlaskDroid architecture for fine-grained access control enforcement on
the Android platform.Comment: Recipient of the Best Paper Awar
The Feasibility of Dynamically Granted Permissions: Aligning Mobile Privacy with User Preferences
Current smartphone operating systems regulate application permissions by
prompting users on an ask-on-first-use basis. Prior research has shown that
this method is ineffective because it fails to account for context: the
circumstances under which an application first requests access to data may be
vastly different than the circumstances under which it subsequently requests
access. We performed a longitudinal 131-person field study to analyze the
contextuality behind user privacy decisions to regulate access to sensitive
resources. We built a classifier to make privacy decisions on the user's behalf
by detecting when context has changed and, when necessary, inferring privacy
preferences based on the user's past decisions and behavior. Our goal is to
automatically grant appropriate resource requests without further user
intervention, deny inappropriate requests, and only prompt the user when the
system is uncertain of the user's preferences. We show that our approach can
accurately predict users' privacy decisions 96.8% of the time, which is a
four-fold reduction in error rate compared to current systems.Comment: 17 pages, 4 figure
Personalisation and recommender systems in digital libraries
Widespread use of the Internet has resulted in digital libraries that are increasingly used by diverse communities of users for diverse purposes and in which sharing and collaboration have become important social elements. As such libraries become commonplace, as their contents and services become more varied, and as their patrons become more experienced with computer technology, users will expect more sophisticated services from these libraries. A simple search function, normally an integral part of any digital library, increasingly leads to user frustration as user needs become more complex and as the volume of managed information increases. Proactive digital libraries, where the library evolves from being passive and untailored, are seen as offering great potential for addressing and overcoming these issues and include techniques such as personalisation and recommender systems. In this paper, following on from the DELOS/NSF Working Group on Personalisation and Recommender Systems for Digital Libraries, which met and reported during 2003, we present some background material on the scope of personalisation and recommender systems in digital libraries. We then outline the working group’s vision for the evolution of digital libraries and the role that personalisation and recommender systems will play, and we present a series of research challenges and specific recommendations and research priorities for the field
Multiple multimodal mobile devices: Lessons learned from engineering lifelog solutions
For lifelogging, or the recording of one’s life history through digital means, to be successful, a range of separate multimodal mobile devices must be employed. These include smartphones such as the N95, the Microsoft SenseCam – a wearable passive photo capture device, or
wearable biometric devices. Each collects a facet of the bigger picture, through, for example, personal digital photos, mobile messages and documents access history, but unfortunately, they operate independently and unaware of each other. This creates significant challenges for the practical application of these devices, the use and integration of their data and their operation by a user. In this chapter we discuss the software engineering challenges and their implications for individuals working on integration of data from multiple ubiquitous mobile devices drawing on our experiences working with such technology over the past several years for the development of integrated personal lifelogs. The chapter serves as an engineering guide to those considering working in the domain of lifelogging and more generally to those working with multiple multimodal devices and integration of their data
The Framework Catalogue of Digital Competences
The Framework Catalogue of Digital Competences
Justyna Jasiewicz, Mirosław Filiciak, Anna Mierzecka, Kamil Śliwowski, Andrzej Klimczuk, Małgorzata Kisilowska, Alek Tarkowski & Jacek Zadrożny
Centrum Cyfrowe Projekt: Polska (2015
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