Abstract: Encryption protects communication partners from
disclosure of their secret messages but cannot prevent traffic analysis
and the leakage of information about “who communicates with
whom". In the presence of collaborating adversaries, this linkability
of actions can danger anonymity. However, reliably providing
anonymity is crucial in many applications. Especially in contextaware
mobile business, where mobile users equipped with PDAs
request and receive services from service providers, providing
anonymous communication is mission-critical and challenging at the
same time. Firstly, the limited performance of mobile devices does
not allow for heavy use of expensive public-key operations which are
commonly used in anonymity protocols. Moreover, the demands for
security depend on the application (e.g., mobile dating vs. pizza
delivery service), but different users (e.g., a celebrity vs. a normal
person) may even require different security levels for the same
application. Considering both hardware limitations of mobile devices
and different sensitivity of users, we propose an anonymity
framework that is dynamically configurable according to user and
application preferences. Our framework is based on Chaum-s mixnet.
We explain the proposed framework, its configuration
parameters for the dynamic behavior and the algorithm to enforce
dynamic anonymity.
Abstract: Policy management in organizations became rising issue in the last decade. It’s because of today’s regulatory requirements in the organizations. To manage policies in large organizations is an imperative work. However, major challenges facing organizations in the last decade is managing all the policies in the organization and making them an active documents rather than simple (inactive) documents stored in computer hard drive or on a shelf. Because of this challenge, organizations need policy management program. This policy management program can be either manual or automated. This paper presents suggestions towards managing policies in organizations. As well as possible policy management solution or program to be utilized, manual or automated. The research first examines the models and frameworks used for managing policies from various perspectives in the literature of the research area/domain. At the end of this paper, a policy management framework is proposed for managing enterprise policies effectively and in a simplified manner.
Abstract: Recent scientific investigations indicate that
multimodal biometrics overcome the technical limitations of
unimodal biometrics, making them ideally suited for everyday life
applications that require a reliable authentication system. However,
for a successful adoption of multimodal biometrics, such systems
would require large heterogeneous datasets with complex multimodal
fusion and privacy schemes spanning various distributed
environments. From experimental investigations of current
multimodal systems, this paper reports the various issues related to
speed, error-recovery and privacy that impede the diffusion of such
systems in real-life. This calls for a robust mechanism that caters to
the desired real-time performance, robust fusion schemes,
interoperability and adaptable privacy policies.
The main objective of this paper is to present a framework that
addresses the abovementioned issues by leveraging on the
heterogeneous resource sharing capacities of Grid services and the
efficient machine learning capabilities of artificial neural networks
(ANN). Hence, this paper proposes a Grid-based neural network
framework for adopting multimodal biometrics with the view of
overcoming the barriers of performance, privacy and risk issues that
are associated with shared heterogeneous multimodal data centres.
The framework combines the concept of Grid services for reliable
brokering and privacy policy management of shared biometric
resources along with a momentum back propagation ANN (MBPANN)
model of machine learning for efficient multimodal fusion and
authentication schemes. Real-life applications would be able to adopt
the proposed framework to cater to the varying business requirements
and user privacies for a successful diffusion of multimodal
biometrics in various day-to-day transactions.