Journal ArticleDOI
RR-LADP: A Privacy-Enhanced Federated Learning Scheme for Internet of Everything
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TLDR
A privacy-enhanced federated learning scheme for IoE using the randomized response (RR) mechanism and the local adaptive differential privacy (LADP) mechanism to prevent the server from knowing whose updates are collected in each round.Abstract:
While the widespread use of ubiquitously connected devices in Internet of Everything (IoE) offers enormous benefits, it also raises serious privacy concerns. Federated learning, as one of the promising solutions to alleviate such problems, is considered as capable of performing data training without exposing raw data that kept by multiple devices. However, either malicious attackers or untrusted servers can deduce users’ privacy from the local updates of each device. Previous studies mainly focus on privacy-preserving approaches inside the servers, which require the framework to be built on trusted servers. In this article, we propose a privacy-enhanced federated learning scheme for IoE. Two mechanisms are adopted in our approach, namely the randomized response (RR) mechanism and the local adaptive differential privacy (LADP) mechanism. RR is adopted to prevent the server from knowing whose updates are collected in each round. LADP enables devices to add noise adaptively to its local updates before submitting them to the server. Experiments demonstrate the feasibility and effectiveness of our approach.read more
Citations
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Journal ArticleDOI
Secure, Privacy Preserving, and Verifiable Federating Learning Using Blockchain for Internet of Vehicles
TL;DR: In this paper , the authors present a practical prospect of blockchain empowered federated learning to realize fully secure, privacy preserving, and verifiable FL for the IoV that is capable of providing secure and trustworthy ITS services.
Journal ArticleDOI
Optimal Multikey Homomorphic Encryption with Steganography Approach for Multimedia Security in Internet of Everything Environment
Ibrahim Abunadi,Hanan Abdullah Mengash,Saud S. Alotaibi,Mashael M. Asiri,Manar Ahmed Hamza,Abu Sarwar Zamani,Abdelwahed Motwakel,Ishfaq Yaseen +7 more
TL;DR: An optimal multikey homomorphic encryption with steganography approach for multimedia security (OMKHES-MS) technique in the IoE environment is proposed and the supremacy of the proposed model over the rennet approaches interms of different measures is reported.
Journal ArticleDOI
Recent trends towards privacy-preservation in Internet of Things, its challenges and future directions
TL;DR: In this article , the authors describe the background of IoT systems and privacy and security measures, including (a) approaches to preserve privacy in IoT-based systems, (b) existing privacy solutions, and (c) recommending privacy models for different layers of IoT applications.
Journal ArticleDOI
Security provisions in smart edge computing devices using blockchain and machine learning algorithms: a novel approach
TL;DR: In this paper , the authors proposed a federated learning approach for data security in a mobile edge based distributive environment using the consensus approach of blockchain and machine learning techniques that include several classifiers and optimization techniques, and applied the proposed algorithms in an edge computing environment by distributing several batches of data to different clients.
Journal ArticleDOI
Toward Privacy Preserving Federated Learning in Internet of Vehicular Things: Challenges and Future Directions
TL;DR: In this paper , a federated graph convolutional recurrent network (Fed-GCRN) is introduced to learn spatial-temporal information for traffic flows forecasting in the Internet of Vehicular Things (IoVT).
References
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Book
The Algorithmic Foundations of Differential Privacy
Cynthia Dwork,Aaron Roth +1 more
TL;DR: The preponderance of this monograph is devoted to fundamental techniques for achieving differential privacy, and application of these techniques in creative combinations, using the query-release problem as an ongoing example.
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TL;DR: In this paper, the authors presented a decentralized approach for federated learning of deep networks based on iterative model averaging, and conduct an extensive empirical evaluation, considering five different model architectures and four datasets.
Proceedings ArticleDOI
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
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Proceedings ArticleDOI
Practical Secure Aggregation for Privacy-Preserving Machine Learning
Keith Bonawitz,Vladimir Ivanov,Ben Kreuter,Antonio Marcedone,H. Brendan McMahan,Sarvar Patel,Daniel Ramage,Aaron Segal,Karn Seth +8 more
TL;DR: In this paper, the authors proposed a secure aggregation of high-dimensional data for federated deep neural networks, which allows a server to compute the sum of large, user-held data vectors from mobile devices in a secure manner without learning each user's individual contribution.
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Our data, ourselves: privacy via distributed noise generation
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