1,792 research outputs found
Security Attacks and Countermeasures in Smart Homes
The Internet of Things (IoT) application is visible in all aspects of humans’ day-to-day affairs. The demand for IoT is growing at an unprecedented rate, from wearable wristwatches to autopilot cars. The smart home has also seen significant advancements to improve the quality of lifestyle. However, the security and privacy of IoT devices have become primary concerns as data is shared among intelligent devices and over the internet in a smart home network. There are several attacks - node capturing attack, sniffing attack, malware attack, boot phase attack, etc., which are conducted by adversaries to breach the security of smart homes. The security breach has a negative impact on the tenants\u27 privacy and prevents the availability of smart home services. This article presents smart homes\u27 most common security attacks and mitigation techniques
An overview of machine learning and 5G for people with disabilities
Currently, over a billion people, including children (or about 15% of the world’s population), are estimated to be living with disability, and this figure is going to increase to beyond two billion by 2050. People with disabilities generally experience poorer levels of health, fewer achievements in education, fewer economic opportunities, and higher rates of poverty. Artificial intelligence and 5G can make major contributions towards the assistance of people with disabilities, so they can achieve a good quality of life. In this paper, an overview of machine learning and 5G for people with disabilities is provided. For this purpose, the proposed 5G network slicing architecture for disabled people is introduced. Different application scenarios and their main benefits are considered to illustrate the interaction of machine learning and 5G. Critical challenges have been identified and addressed.This work has been supported by the Agencia Estatal de Investigación of Ministerio de Ciencia e Innovación of Spain under project PID2019-108713RB-C51 MCIN/ AEI /10.13039/501100011033.Postprint (published version
On Studying Distributed Machine Learning
The Internet of Things (IoT) is utilizing Deep Learning (DL) for applications such as voice or image recognition. Processing data for DL directly on IoT edge devices reduces latency and increases privacy. To overcome the resource constraints of IoT edge devices, the computation for DL inference is distributed between a cluster of several devices. This paper explores DL, IoT networks, and a novel framework for distributed processing of DL in IoT clusters. The aim is to facilitate and simplify deployment, testing, and study of a distributed DL system, even without physical devices. The contributions of this paper are a deployment of the framework to an Ubuntu virtual machine testbed and a repackaging of the framework as a Docker image for portability and fast future deployment
Paralinguistic Privacy Protection at the Edge
Voice user interfaces and digital assistants are rapidly entering our lives
and becoming singular touch points spanning our devices. These always-on
services capture and transmit our audio data to powerful cloud services for
further processing and subsequent actions. Our voices and raw audio signals
collected through these devices contain a host of sensitive paralinguistic
information that is transmitted to service providers regardless of deliberate
or false triggers. As our emotional patterns and sensitive attributes like our
identity, gender, mental well-being, are easily inferred using deep acoustic
models, we encounter a new generation of privacy risks by using these services.
One approach to mitigate the risk of paralinguistic-based privacy breaches is
to exploit a combination of cloud-based processing with privacy-preserving,
on-device paralinguistic information learning and filtering before transmitting
voice data. In this paper we introduce EDGY, a configurable, lightweight,
disentangled representation learning framework that transforms and filters
high-dimensional voice data to identify and contain sensitive attributes at the
edge prior to offloading to the cloud. We evaluate EDGY's on-device performance
and explore optimization techniques, including model quantization and knowledge
distillation, to enable private, accurate and efficient representation learning
on resource-constrained devices. Our results show that EDGY runs in tens of
milliseconds with 0.2% relative improvement in ABX score or minimal performance
penalties in learning linguistic representations from raw voice signals, using
a CPU and a single-core ARM processor without specialized hardware.Comment: 14 pages, 7 figures. arXiv admin note: text overlap with
arXiv:2007.1506
Biometrics for internet‐of‐things security: A review
The large number of Internet‐of‐Things (IoT) devices that need interaction between smart devices and consumers makes security critical to an IoT environment. Biometrics offers an interesting window of opportunity to improve the usability and security of IoT and can play a significant role in securing a wide range of emerging IoT devices to address security challenges. The purpose of this review is to provide a comprehensive survey on the current biometrics research in IoT security, especially focusing on two important aspects, authentication and encryption. Regarding authentication, contemporary biometric‐based authentication systems for IoT are discussed and classified based on different biometric traits and the number of biometric traits employed in the system. As for encryption, biometric‐cryptographic systems, which integrate biometrics with cryptography and take advantage of both to provide enhanced security for IoT, are thoroughly reviewed and discussed. Moreover, challenges arising from applying biometrics to IoT and potential solutions are identified and analyzed. With an insight into the state‐of‐the‐art research in biometrics for IoT security, this review paper helps advance the study in the field and assists researchers in gaining a good understanding of forward‐looking issues and future research directions
Taxonomic Classification of IoT Smart Home Voice Control
Voice control in the smart home is commonplace, enabling the convenient
control of smart home Internet of Things hubs, gateways and devices, along with
information seeking dialogues. Cloud-based voice assistants are used to
facilitate the interaction, yet privacy concerns surround the cloud analysis of
data. To what extent can voice control be performed using purely local
computation, to ensure user data remains private? In this paper we present a
taxonomy of the voice control technologies present in commercial smart home
systems. We first review literature on the topic, and summarise relevant work
categorising IoT devices and voice control in the home. The taxonomic
classification of these entities is then presented, and we analyse our
findings. Following on, we turn to academic efforts in implementing and
evaluating voice-controlled smart home set-ups, and we then discuss open-source
libraries and devices that are applicable to the design of a privacy-preserving
voice assistant for smart homes and the IoT. Towards the end, we consider
additional technologies and methods that could support a cloud-free voice
assistant, and conclude the work
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