4,107 research outputs found
The Transitivity of Trust Problem in the Interaction of Android Applications
Mobile phones have developed into complex platforms with large numbers of
installed applications and a wide range of sensitive data. Application security
policies limit the permissions of each installed application. As applications
may interact, restricting single applications may create a false sense of
security for the end users while data may still leave the mobile phone through
other applications. Instead, the information flow needs to be policed for the
composite system of applications in a transparent and usable manner. In this
paper, we propose to employ static analysis based on the software architecture
and focused data flow analysis to scalably detect information flows between
components. Specifically, we aim to reveal transitivity of trust problems in
multi-component mobile platforms. We demonstrate the feasibility of our
approach with Android applications, although the generalization of the analysis
to similar composition-based architectures, such as Service-oriented
Architecture, can also be explored in the future
Secure Cloud-Edge Deployments, with Trust
Assessing the security level of IoT applications to be deployed to
heterogeneous Cloud-Edge infrastructures operated by different providers is a
non-trivial task. In this article, we present a methodology that permits to
express security requirements for IoT applications, as well as infrastructure
security capabilities, in a simple and declarative manner, and to automatically
obtain an explainable assessment of the security level of the possible
application deployments. The methodology also considers the impact of trust
relations among different stakeholders using or managing Cloud-Edge
infrastructures. A lifelike example is used to showcase the prototyped
implementation of the methodology
Trust and Privacy Permissions for an Ambient World
Ambient intelligence (AmI) and ubiquitous computing allow us to consider a future where computation is embedded into our daily social lives. This vision raises its own important questions and augments the need to understand how people will trust such systems and at the same time achieve and maintain privacy. As a result, we have recently conducted a wide reaching study of people’s attitudes to potential AmI scenarios with a view to eliciting their privacy concerns. This chapter describes recent research related to privacy and trust with regard to ambient technology. The method used in the study is described and findings discussed
Trustworthy content push
Delivery of content to mobile devices gains increasing importance in
industrial environments to support employees in the field. An important
application are e-mail push services like the fashionable Blackberry. These
systems are facing security challenges regarding data transport to, and storage
of the data on the end user equipment. The emerging Trusted Computing
technology offers new answers to these open questions.Comment: 4 pages, 4 eps figure
Trust beyond reputation: A computational trust model based on stereotypes
Models of computational trust support users in taking decisions. They are
commonly used to guide users' judgements in online auction sites; or to
determine quality of contributions in Web 2.0 sites. However, most existing
systems require historical information about the past behavior of the specific
agent being judged. In contrast, in real life, to anticipate and to predict a
stranger's actions in absence of the knowledge of such behavioral history, we
often use our "instinct"- essentially stereotypes developed from our past
interactions with other "similar" persons. In this paper, we propose
StereoTrust, a computational trust model inspired by stereotypes as used in
real-life. A stereotype contains certain features of agents and an expected
outcome of the transaction. When facing a stranger, an agent derives its trust
by aggregating stereotypes matching the stranger's profile. Since stereotypes
are formed locally, recommendations stem from the trustor's own personal
experiences and perspective. Historical behavioral information, when available,
can be used to refine the analysis. According to our experiments using
Epinions.com dataset, StereoTrust compares favorably with existing trust models
that use different kinds of information and more complete historical
information
Portunes: analyzing multi-domain insider threats
The insider threat is an important problem in securing information systems. Skilful insiders use attack vectors that yield the greatest chance of success, and thus do not limit themselves to a restricted set of attacks. They may use access rights to the facility where the system of interest resides, as well as existing relationships with employees. To secure a system, security professionals should therefore consider attacks that include non-digital aspects such as key sharing or exploiting trust relationships among employees. In this paper, we present Portunes, a framework for security design and audit, which incorporates three security domains: (1) the security of the computer system itself (the digital domain), (2) the security of the location where the system is deployed (the physical domain) and (3) the security awareness of the employees that use the system (the social domain). The framework consists of a model, a formal language and a logic. It allows security professionals to formally model elements from the three domains in a single framework, and to analyze possible attack scenarios. The logic enables formal specification of the attack scenarios in terms of state and transition properties
Flow-based reputation: more than just ranking
The last years have seen a growing interest in collaborative systems like
electronic marketplaces and P2P file sharing systems where people are intended
to interact with other people. Those systems, however, are subject to security
and operational risks because of their open and distributed nature. Reputation
systems provide a mechanism to reduce such risks by building trust
relationships among entities and identifying malicious entities. A popular
reputation model is the so called flow-based model. Most existing reputation
systems based on such a model provide only a ranking, without absolute
reputation values; this makes it difficult to determine whether entities are
actually trustworthy or untrustworthy. In addition, those systems ignore a
significant part of the available information; as a consequence, reputation
values may not be accurate. In this paper, we present a flow-based reputation
metric that gives absolute values instead of merely a ranking. Our metric makes
use of all the available information. We study, both analytically and
numerically, the properties of the proposed metric and the effect of attacks on
reputation values
Sea of Lights: Practical Device-to-Device Security Bootstrapping in the Dark
Practical solutions to bootstrap security in today's information and
communication systems critically depend on centralized services for
authentication as well as key and trust management. This is particularly true
for mobile users. Identity providers such as Google or Facebook have active
user bases of two billion each, and the subscriber number of mobile operators
exceeds five billion unique users as of early 2018. If these centralized
services go completely `dark' due to natural or man made disasters, large scale
blackouts, or country-wide censorship, the users are left without practical
solutions to bootstrap security on their mobile devices. Existing distributed
solutions, for instance, the so-called web-of-trust are not sufficiently
lightweight. Furthermore, they support neither cross-application on mobile
devices nor strong protection of key material using hardware security modules.
We propose Sea of Lights(SoL), a practical lightweight scheme for bootstrapping
device-to-device security wirelessly, thus, enabling secure distributed
self-organized networks. It is tailored to operate `in the dark' and provides
strong protection of key material as well as an intuitive means to build a
lightweight web-of-trust. SoL is particularly well suited for local or urban
operation in scenarios such as the coordination of emergency response, where it
helps containing/limiting the spreading of misinformation. As a proof of
concept, we implement SoL in the Android platform and hence test its
feasibility on real mobile devices. We further evaluate its key performance
aspects using simulation
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