8,360 research outputs found
AMISEC: Leveraging Redundancy and Adaptability to Secure AmI Applications
Security in Ambient Intelligence (AmI) poses too many challenges due to the inherently insecure nature of wireless sensor nodes. However, there are two characteristics of these environments that can be used effectively to prevent, detect, and confine attacks: redundancy and continuous adaptation. In this article we propose a global strategy and a system architecture to cope with security issues in AmI applications at different levels. Unlike in previous approaches, we assume an individual wireless node is vulnerable. We present an agent-based architecture with supporting services that is proven to be adequate to detect and confine common attacks. Decisions at different levels are supported by a trust-based framework with good and bad reputation feedback while maintaining resistance to bad-mouthing attacks. We also propose a set of services that can be used to handle identification, authentication, and authorization in intelligent ambients. The resulting approach takes into account practical issues, such as resource limitation, bandwidth optimization, and scalability
A NOVEL FRAMEWORK FOR SOCIAL INTERNET OF THINGS: LEVERAGING THE FRIENDSHIPS AND THE SERVICES EXCHANGED BETWEEN SMART DEVICES
As humans, we tackle many problems in complex societies and manage the complexities of networked social systems. Cognition and sociability are two vital human capabilities that improve social life and complex social interactions. Adding these features to smart devices makes them capable of managing complex and networked Internet of Things (IoT) settings.
Cognitive and social devices can improve their relationships and connections with other devices and people to better serve human needs. Nowadays, researchers are investigating two future generations of IoT: social IoT (SIoT) and cognitive IoT (CIoT). This study develops a new framework for IoT, called CSIoT, by using complexity science concepts and by integrating social and cognitive IoT concepts. This framework uses a new mechanism to leverage the friendships between devices to address service management, privacy, and security. The framework addresses network navigability, resilience, and heterogeneity between devices in IoT settings. This study uses a new simulation tool for evaluating the new CSIoT framework and evaluates the privacy-preserving ability of CSIoT using the new simulation tool. To address different CSIoT security and privacy issues, this study also proposes a blockchain-based CSIoT. The evaluation results show that CSIoT can effectively preserve the privacy and the blockchain-based CSIoT performs effectively in addressing different privacy and security issues
k-Same-Siamese-GAN: k-Same Algorithm with Generative Adversarial Network for Facial Image De-identification with Hyperparameter Tuning and Mixed Precision Training
For a data holder, such as a hospital or a government entity, who has a
privately held collection of personal data, in which the revealing and/or
processing of the personal identifiable data is restricted and prohibited by
law. Then, "how can we ensure the data holder does conceal the identity of each
individual in the imagery of personal data while still preserving certain
useful aspects of the data after de-identification?" becomes a challenge issue.
In this work, we propose an approach towards high-resolution facial image
de-identification, called k-Same-Siamese-GAN, which leverages the
k-Same-Anonymity mechanism, the Generative Adversarial Network, and the
hyperparameter tuning methods. Moreover, to speed up model training and reduce
memory consumption, the mixed precision training technique is also applied to
make kSS-GAN provide guarantees regarding privacy protection on close-form
identities and be trained much more efficiently as well. Finally, to validate
its applicability, the proposed work has been applied to actual datasets - RafD
and CelebA for performance testing. Besides protecting privacy of
high-resolution facial images, the proposed system is also justified for its
ability in automating parameter tuning and breaking through the limitation of
the number of adjustable parameters
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