5,389 research outputs found
ProCMotive: Bringing Programability and Connectivity into Isolated Vehicles
In recent years, numerous vehicular technologies, e.g., cruise control and
steering assistant, have been proposed and deployed to improve the driving
experience, passenger safety, and vehicle performance. Despite the existence of
several novel vehicular applications in the literature, there still exists a
significant gap between resources needed for a variety of vehicular (in
particular, data-dominant, latency-sensitive, and computationally-heavy)
applications and the capabilities of already-in-market vehicles. To address
this gap, different smartphone-/Cloud-based approaches have been proposed that
utilize the external computational/storage resources to enable new
applications. However, their acceptance and application domain are still very
limited due to programability, wireless connectivity, and performance
limitations, along with several security/privacy concerns. In this paper, we
present a novel architecture that can potentially enable rapid development of
various vehicular applications while addressing shortcomings of
smartphone-/Cloud-based approaches. The architecture is formed around a core
component, called SmartCore, a privacy/security-friendly programmable dongle
that brings general-purpose computational and storage resources to the vehicle
and hosts in-vehicle applications. Based on the proposed architecture, we
develop an application development framework for vehicles, that we call
ProCMotive. ProCMotive enables developers to build customized vehicular
applications along the Cloud-to-edge continuum, i.e., different functions of an
application can be distributed across SmartCore, the user's personal devices,
and the Cloud. To highlight potential benefits that the framework provides, we
design and develop two different vehicular applications based on ProCMotive,
namely, Amber Response and Insurance Monitor.Comment: 23 Page
Systematic Review on Security and Privacy Requirements in Edge Computing: State of the Art and Future Research Opportunities
Edge computing is a promising paradigm that enhances the capabilities of cloud computing. In order to continue patronizing the computing services, it is essential to conserve a good atmosphere free from all kinds of security and privacy breaches. The security and privacy issues associated with the edge computing environment have narrowed the overall acceptance of the technology as a reliable paradigm. Many researchers have reviewed security and privacy issues in edge computing, but not all have fully investigated the security and privacy requirements. Security and privacy requirements are the objectives that indicate the capabilities as well as functions a system performs in eliminating certain security and privacy vulnerabilities. The paper aims to substantially review the security and privacy requirements of the edge computing and the various technological methods employed by the techniques used in curbing the threats, with the aim of helping future researchers in identifying research opportunities. This paper investigate the current studies and highlights the following: (1) the classification of security and privacy requirements in edge computing, (2) the state of the art techniques deployed in curbing the security and privacy threats, (3) the trends of technological methods employed by the techniques, (4) the metrics used for evaluating the performance of the techniques, (5) the taxonomy of attacks affecting the edge network, and the corresponding technological trend employed in mitigating the attacks, and, (6) research opportunities for future researchers in the area of edge computing security and privacy
Edge analytics in the internet of things
High-data-rate sensors are becoming ubiquitous in the Internet of Things. GigaSight is an Internet-scale repository of crowd-sourced video content that enforces privacy preferences and access controls. The architecture is a federated system of VM-based cloudlets that perform video analytics at the edge of the Internet
Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
With the breakthroughs in deep learning, the recent years have witnessed a
booming of artificial intelligence (AI) applications and services, spanning
from personal assistant to recommendation systems to video/audio surveillance.
More recently, with the proliferation of mobile computing and
Internet-of-Things (IoT), billions of mobile and IoT devices are connected to
the Internet, generating zillions Bytes of data at the network edge. Driving by
this trend, there is an urgent need to push the AI frontiers to the network
edge so as to fully unleash the potential of the edge big data. To meet this
demand, edge computing, an emerging paradigm that pushes computing tasks and
services from the network core to the network edge, has been widely recognized
as a promising solution. The resulted new inter-discipline, edge AI or edge
intelligence, is beginning to receive a tremendous amount of interest. However,
research on edge intelligence is still in its infancy stage, and a dedicated
venue for exchanging the recent advances of edge intelligence is highly desired
by both the computer system and artificial intelligence communities. To this
end, we conduct a comprehensive survey of the recent research efforts on edge
intelligence. Specifically, we first review the background and motivation for
artificial intelligence running at the network edge. We then provide an
overview of the overarching architectures, frameworks and emerging key
technologies for deep learning model towards training/inference at the network
edge. Finally, we discuss future research opportunities on edge intelligence.
We believe that this survey will elicit escalating attentions, stimulate
fruitful discussions and inspire further research ideas on edge intelligence.Comment: Zhi Zhou, Xu Chen, En Li, Liekang Zeng, Ke Luo, and Junshan Zhang,
"Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge
Computing," Proceedings of the IEE
Internet of Things: An Overview
As technology proceeds and the number of smart devices continues to grow
substantially, need for ubiquitous context-aware platforms that support
interconnected, heterogeneous, and distributed network of devices has given
rise to what is referred today as Internet-of-Things. However, paving the path
for achieving aforementioned objectives and making the IoT paradigm more
tangible requires integration and convergence of different knowledge and
research domains, covering aspects from identification and communication to
resource discovery and service integration. Through this chapter, we aim to
highlight researches in topics including proposed architectures, security and
privacy, network communication means and protocols, and eventually conclude by
providing future directions and open challenges facing the IoT development.Comment: Keywords: Internet of Things; IoT; Web of Things; Cloud of Thing
Confused Modulo Projection based Somewhat Homomorphic Encryption -- Cryptosystem, Library and Applications on Secure Smart Cities
With the development of cloud computing, the storage and processing of
massive visual media data has gradually transferred to the cloud server. For
example, if the intelligent video monitoring system cannot process a large
amount of data locally, the data will be uploaded to the cloud. Therefore, how
to process data in the cloud without exposing the original data has become an
important research topic. We propose a single-server version of somewhat
homomorphic encryption cryptosystem based on confused modulo projection theorem
named CMP-SWHE, which allows the server to complete blind data processing
without \emph{seeing} the effective information of user data. On the client
side, the original data is encrypted by amplification, randomization, and
setting confusing redundancy. Operating on the encrypted data on the server
side is equivalent to operating on the original data. As an extension, we
designed and implemented a blind computing scheme of accelerated version based
on batch processing technology to improve efficiency. To make this algorithm
easy to use, we also designed and implemented an efficient general blind
computing library based on CMP-SWHE. We have applied this library to foreground
extraction, optical flow tracking and object detection with satisfactory
results, which are helpful for building smart cities. We also discuss how to
extend the algorithm to deep learning applications. Compared with other
homomorphic encryption cryptosystems and libraries, the results show that our
method has obvious advantages in computing efficiency. Although our algorithm
has some tiny errors () when the data is too large, it is very
efficient and practical, especially suitable for blind image and video
processing.Comment: IEEE Internet of Things Journal (IOTJ), Published Online: 7 August
202
Recent Developments in Cloud Based Systems: State of Art
Cloud computing is the new buzzword in the head of the techies round the
clock these days. The importance and the different applications of cloud
computing are overwhelming and thus, it is a topic of huge significance. It
provides several astounding features like Multitenancy, on demand service, pay
per use etc. This manuscript presents an exhaustive survey on cloud computing
technology and potential research issues in cloud computing that needs to be
addressed
Cloud Forensics: A Meta-Study of Challenges, Approaches, and Open Problems
In recent years, cloud computing has become popular as a cost-effective and
efficient computing paradigm. Unfortunately, today's cloud computing
architectures are not designed for security and forensics. To date, very little
research has been done to develop the theory and practice of cloud forensics.
Many factors complicate forensic investigations in a cloud environment. First,
the storage system is no longer local. Therefore, even with a subpoena, law
enforcement agents cannot confiscate the suspect's computer and get access to
the suspect's files. Second, each cloud server contains files from many users.
Hence, it is not feasible to seize servers from a data center without violating
the privacy of many other users. Third, even if the data belonging to a
particular suspect is identified, separating it from other users' data is
difficult. Moreover, other than the cloud provider's word, there is usually no
evidence that links a given data file to a particular suspect. For such
challenges, clouds cannot be used to store healthcare, business, or national
security related data, which require audit and regulatory compliance. In this
paper, we systematically examine the cloud forensics problem and explore the
challenges and issues in cloud forensics. We then discuss existing research
projects and finally, we highlight the open problems and future directions in
cloud forensics research area. We posit that our systematic approach towards
understanding the nature and challenges of cloud forensics will allow us to
examine possible secure solution approaches, leading to increased trust on and
adoption of cloud computing, especially in business, healthcare, and national
security. This in turn will lead to lower cost and long-term benefit to our
society as a whole
RaSEC : an intelligent framework for reliable and secure multilevel edge computing in industrial environments
Industrial applications generate big data with redundant information that is transmitted over heterogeneous networks. The transmission of big data with redundant information not only increases the overall end-to-end delay but also increases the computational load on servers which affects the performance of industrial applications. To address these challenges, we propose an intelligent framework named Reliable and Secure multi-level Edge Computing (RaSEC), which operates in three phases. In the first phase, level-one edge devices apply a lightweight aggregation technique on the generated data. This technique not only reduces the size of the generated data but also helps in preserving the privacy of data sources. In the second phase, a multistep process is used to register level-two edge devices (LTEDs) with high-level edge devices (HLEDs). Due to the registration process, only legitimate LTEDs can forward data to the HLEDs, and as a result, the computational load on HLEDs decreases. In the third phase, the HLEDs use a convolutional neural network to detect the presence of moving objects in the data forwarded by LTEDs. If a movement is detected, the data is uploaded to the cloud servers for further analysis; otherwise, the data is discarded to minimize the use of computational resources on cloud computing platforms. The proposed framework reduces the response time by forwarding useful information to the cloud servers and can be utilized by various industrial applications. Our theoretical and experimental results confirm the resiliency of our framework with respect to security and privacy threats. © 1972-2012 IEEE
AFFECT-PRESERVING VISUAL PRIVACY PROTECTION
The prevalence of wireless networks and the convenience of mobile cameras enable many new video applications other than security and entertainment. From behavioral diagnosis to wellness monitoring, cameras are increasing used for observations in various educational and medical settings. Videos collected for such applications are considered protected health information under privacy laws in many countries. Visual privacy protection techniques, such as blurring or object removal, can be used to mitigate privacy concern, but they also obliterate important visual cues of affect and social behaviors that are crucial for the target applications. In this dissertation, we propose to balance the privacy protection and the utility of the data by preserving the privacy-insensitive information, such as pose and expression, which is useful in many applications involving visual understanding.
The Intellectual Merits of the dissertation include a novel framework for visual privacy protection by manipulating facial image and body shape of individuals, which: (1) is able to conceal the identity of individuals; (2) provide a way to preserve the utility of the data, such as expression and pose information; (3) balance the utility of the data and capacity of the privacy protection.
The Broader Impacts of the dissertation focus on the significance of privacy protection on visual data, and the inadequacy of current privacy enhancing technologies in preserving affect and behavioral attributes of the visual content, which are highly useful for behavior observation in educational and medical settings. This work in this dissertation represents one of the first attempts in achieving both goals simultaneously
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