492 research outputs found
A survey of machine and deep learning methods for privacy protection in the Internet of things
Recent advances in hardware and information technology have accelerated the proliferation of smart and interconnected devices facilitating the rapid development of the Internet of Things (IoT). IoT applications and services are widely adopted in environments such as smart cities, smart industry, autonomous vehicles, and eHealth. As such, IoT devices are ubiquitously connected, transferring sensitive and personal data without requiring human interaction. Consequently, it is crucial to preserve data privacy. This paper presents a comprehensive survey of recent Machine Learning (ML)- and Deep Learning (DL)-based solutions for privacy in IoT. First, we present an in depth analysis of current privacy threats and attacks. Then, for each ML architecture proposed, we present the implementations, details, and the published results. Finally, we identify the most effective solutions for the different threats and attacks.This work is partially supported by the Generalitat de Catalunya under grant 2017 SGR 962 and the HORIZON-GPHOENIX (101070586) and HORIZON-EUVITAMIN-V (101093062) projects.Peer ReviewedPostprint (published version
Privacy-Preserving Cloud-Assisted Data Analytics
Nowadays industries are collecting a massive and exponentially growing amount of data that can be utilized to extract useful insights for improving various aspects of our life. Data analytics (e.g., via the use of machine learning) has been extensively applied to make important decisions in various real world applications. However, it is challenging for resource-limited clients to analyze their data in an efficient way when its scale is large. Additionally, the data resources are increasingly distributed among different owners. Nonetheless, users\u27 data may contain private information that needs to be protected.
Cloud computing has become more and more popular in both academia and industry communities. By pooling infrastructure and servers together, it can offer virtually unlimited resources easily accessible via the Internet. Various services could be provided by cloud platforms including machine learning and data analytics.
The goal of this dissertation is to develop privacy-preserving cloud-assisted data analytics solutions to address the aforementioned challenges, leveraging the powerful and easy-to-access cloud. In particular, we propose the following systems.
To address the problem of limited computation power at user and the need of privacy protection in data analytics, we consider geometric programming (GP) in data analytics, and design a secure, efficient, and verifiable outsourcing protocol for GP. Our protocol consists of a transform scheme that converts GP to DGP, a transform scheme with computationally indistinguishability, and an efficient scheme to solve the transformed DGP at the cloud side with result verification. Evaluation results show that the proposed secure outsourcing protocol can achieve significant time savings for users.
To address the problem of limited data at individual users, we propose two distributed learning systems such that users can collaboratively train machine learning models without losing privacy. The first one is a differentially private framework to train logistic regression models with distributed data sources. We employ the relevance between input data features and the model output to significantly improve the learning accuracy. Moreover, we adopt an evaluation data set at the cloud side to suppress low-quality data sources and propose a differentially private mechanism to protect user\u27s data quality privacy. Experimental results show that the proposed framework can achieve high utility with low quality data, and strong privacy guarantee.
The second one is an efficient privacy-preserving federated learning system that enables multiple edge users to collaboratively train their models without revealing dataset. To reduce the communication overhead, we select well-aligned and large-enough magnitude gradients for uploading which leads to quick convergence. To minimize the noise added and improve model utility, each user only adds a small amount of noise to his selected gradients, encrypts the noise gradients before uploading, and the cloud server will only get the aggregate gradients that contain enough noise to achieve differential privacy. Evaluation results show that the proposed system can achieve high accuracy, low communication overhead, and strong privacy guarantee.
In future work, we plan to design a privacy-preserving data analytics with fair exchange, which ensures the payment fairness. We will also consider designing distributed learning systems with heterogeneous architectures
Revealing the Landscape of Privacy-Enhancing Technologies in the Context of Data Markets for the IoT: A Systematic Literature Review
IoT data markets in public and private institutions have become increasingly
relevant in recent years because of their potential to improve data
availability and unlock new business models. However, exchanging data in
markets bears considerable challenges related to disclosing sensitive
information. Despite considerable research focused on different aspects of
privacy-enhancing data markets for the IoT, none of the solutions proposed so
far seems to find a practical adoption. Thus, this study aims to organize the
state-of-the-art solutions, analyze and scope the technologies that have been
suggested in this context, and structure the remaining challenges to determine
areas where future research is required. To accomplish this goal, we conducted
a systematic literature review on privacy enhancement in data markets for the
IoT, covering 50 publications dated up to July 2020, and provided updates with
24 publications dated up to May 2022. Our results indicate that most research
in this area has emerged only recently, and no IoT data market architecture has
established itself as canonical. Existing solutions frequently lack the
required combination of anonymization and secure computation technologies.
Furthermore, there is no consensus on the appropriate use of blockchain
technology for IoT data markets and a low degree of leveraging existing
libraries or reusing generic data market architectures. We also identified
significant challenges remaining, such as the copy problem and the recursive
enforcement problem that-while solutions have been suggested to some extent-are
often not sufficiently addressed in proposed designs. We conclude that
privacy-enhancing technologies need further improvements to positively impact
data markets so that, ultimately, the value of data is preserved through data
scarcity and users' privacy and businesses-critical information are protected.Comment: 49 pages, 17 figures, 11 table
Edge Intelligence : Empowering Intelligence to the Edge of Network
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe
Privacy in the internet of Things. Fostering user empowerment fhrough digital literacy
Os dispositivos da Internet das Coisas estão por todo o lado, desde o nascimento da computação
ubÃqua que se prevê que a vida quotidiana do ser humano contenha milhões de dispositivos que con trolam todos os aspectos da nossa vida. Hoje em dia, temos veÃculos inteligentes, casas inteligentes,
cidades inteligentes, dispositivos vestÃveis, entre outros, que utilizam vários tipos de dispositivos e
vários tipos de redes para comunicar. Estes dispositivos criam novas formas de recolha e tratamento
de dados pessoais de utilizadores e não utilizadores. A maioria dos utilizadores nais nem sequer
tem conhecimento ou tem pouco controlo sobre a informação que está a ser recolhida por estes
sistemas. Este trabalho adopta uma abordagem holÃstica a este problema, começando por realizar
uma revisão da literatura para compilar as soluções actuais, os desa os e as oportunidades de
investigação futura. Realizando, em seguida, um inquérito para saber mais sobre o conhecimento
geral dos indivÃduos acerca da privacidade, da Internet das Coisas e hábitos online e, nalmente,
com base na informação recolhida, é proposta uma aplicação móvel que fornece aos utilizadores
informações sobre os dispositivos que estão próximos e como proteger os dados que não querem
partilhar com estes dispositivos. Os testes com utilizadores revelaram que os participantes val orizam ter acesso a mais informações sobre termos relacionados com a privacidade. Esta aplicação
é capaz de detetar que tipo de dispositivos estão próximos, que tipo de dados são recolhidos por
esses dispositivos e apresentar opções de privacidade ao utilizador, quando possÃvel, com o objetivo
de fornecer aos indivÃduos uma ferramenta para tomarem decisões informadas sobre os seus dados
privados.Internet of Things devices are everywhere, since the birth of ubiquitous computing, human everyday
life is expected to contain millions of devices that control every aspect of our lives. Today we
have smart vehicles, smart houses, smart cities, wearables among other things that use various
types of devices, and various types of networks to communicate. These devices create new ways
of collecting and processing personal data from users, and non-users. Most end users are not even
aware or have little control over the information that is being collected by these systems. This work
takes a holistic approach to this problem by rst conducting a literature review to compile current
solutions, challenges and future research opportunities. Then conducting a survey to learn more
about the general knowledge of individuals about privacy, the Internet of Things and online habits,
and nally, based on the information gathered, a mobile application is proposed that gives users
information about nearby devices, and how to protect the data that they do not want to share
with them. User testing revealed that participants valued having access to more information about
privacy related terms. This application is capable of detecting what type of devices are nearby,
what kind of data is collected by these devices, and displaying privacy options to the user, when it
is possible to do so, with the goal of providing individuals a tool to make informed decisions about
their private data
Edge Intelligence : Empowering Intelligence to the Edge of Network
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe
Application of Computer Vision and Mobile Systems in Education: A Systematic Review
The computer vision industry has experienced a significant surge in growth, resulting in numerous promising breakthroughs in computer intelligence. The present review paper outlines the advantages and potential future implications of utilizing this technology in education. A total of 84 research publications have been thoroughly scrutinized and analyzed. The study revealed that computer vision technology integrated with a mobile application is exceptionally useful in monitoring students’ perceptions and mitigating academic dishonesty. Additionally, it facilitates the digitization of handwritten scripts for plagiarism detection and automates attendance tracking to optimize valuable classroom time. Furthermore, several potential applications of computer vision technology for educational institutions have been proposed to enhance students’ learning processes in various faculties, such as engineering, medical science, and others. Moreover, the technology can also aid in creating a safer campus environment by automatically detecting abnormal activities such as ragging, bullying, and harassment
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