Local Differential Privacy-Based Federated Learning for Internet of Things

@article{Zhao2021LocalDP,
  title={Local Differential Privacy-Based Federated Learning for Internet of Things},
  author={Yang Zhao and Jun Zhao and Mengmeng Yang and Teng Wang and Ning Wang and Lingjuan Lyu and Dusit Tao Niyato and Kwok-Yan Lam},
  journal={IEEE Internet of Things Journal},
  year={2021},
  volume={8},
  pages={8836-8853}
}
The Internet of Vehicles (IoV) is a promising branch of the Internet of Things. IoV simulates a large variety of crowdsourcing applications, such as Waze, Uber, and Amazon Mechanical Turk, etc. Users of these applications report the real-time traffic information to the cloud server which trains a machine learning model based on traffic information reported by users for intelligent traffic management. However, crowdsourcing application owners can easily infer users’ location information, traffic… Expand
FLaaS: Federated Learning as a Service
TLDR
Federated Learning as a Service (FLaaS) is presented, a system enabling different scenarios of 3rd-party application collaborative model building and addressing the consequent challenges of permission and privacy management, usability, and hierarchical model training, and FLaaS can be deployed in different operational environments. Expand
SoK: Training Machine Learning Models over Multiple Sources with Privacy Preservation
TLDR
This work defines the problem of training machine learning models over multiple data sources with privacy-preserving (TMMPP), and compares the recent studies of TMMPP from the aspects of the technical routes, parties supported, data partitioning, threat model, and supported machinelearning models, to show the advantages and limitations. Expand
Local Differential Privacy and Its Applications: A Comprehensive Survey
TLDR
This survey provides a comprehensive and structured overview of the local differential privacy technology and summarise and analyze state-of-the-art research in LDP and compare a range of methods in the context of answering a variety of queries and training different machine learning models. Expand
Gain without Pain: Offsetting DP-injected Nosies Stealthily in Cross-device Federated Learning
TLDR
This work proposes a novel Noise Information Secretly Sharing algorithm (NISS) algorithm to alleviate the disturbance of DP noises by sharing negated noises among clients and theoretically proves that if clients are trustworthy, DP noises can be perfectly offset on the PS. Expand
Privacy and Robustness in Federated Learning: Attacks and Defenses
TLDR
This paper conducts the first comprehensive survey on federated learning, and provides a concise introduction to the concept of FL, and a unique taxonomy covering: 1) threat models; 2) poisoning attacks and defense against robustness; 3) inference attacks and defenses against privacy. Expand
Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications
TLDR
This article aims to provide a holistic overview of relevant communication and ML principles and present communication-efficient and distributed learning frameworks with selected use cases. Expand
Privacy-Preserving High-dimensional Data Collection with Federated Generative Autoencoder
  • Xue Jiang, Xuebing Zhou, Jens Grossklags
  • Computer Science
  • Proc. Priv. Enhancing Technol.
  • 2022
Abstract Business intelligence and AI services often involve the collection of copious amounts of multidimensional personal data. Since these data usually contain sensitive information ofExpand
A Comprehensive Survey of Privacy-preserving Federated Learning
  • Xuefei Yin, Yanming Zhu, Jiankun Hu
  • Computer Science
  • ACM Comput. Surv.
  • 2021
TLDR
A comprehensive and systematic survey on the PPFL based on the proposed 5W-scenario-based taxonomy is presented, which analyze the privacy leakage risks in the FL from five aspects, summarize existing methods, and identify future research directions. Expand
A Contemplative Perspective on Federated Machine Learning: Taxonomy, Threats & Vulnerability Assessment and Challenges
TLDR
An in-depth and comprehensive analysis of Federated Learning and its taxonomy is given and a detailed vulnerability assessment is provided to make federated learning a more functional, robust and secure method to train machine learning models. Expand
A Coordinated and Optimized Mechanism of Artificial Intelligence for Student Management by College Counselors Based on Big Data
  • Zhen Yang, Muhammad Talha
  • Computational and Mathematical Methods in Medicine
  • 2021
The purpose of this article is to perform in-depth research and analysis on the artificial intelligence coordination and optimization mechanism of college counseling student management using big dataExpand
...
1
2
3
4
...

References

SHOWING 1-10 OF 85 REFERENCES
A survey of local differential privacy for securing internet of vehicles
TLDR
This paper gives an overview of the existing LDP techniques and presents the thorough comparisons of these work in terms of advantages, disadvantages, and computation cost, in order to get the readers well acquainted with LDP. Expand
Local Differential Privacy for Deep Learning
TLDR
A new local differentially private (LDP) algorithm named LATENT is proposed that redesigns the training process and enables a data owner to add a randomization layer before data leave the data owners’ devices and reach a potentially untrusted machine learning service. Expand
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
TLDR
In a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved, this raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. Expand
Federated learning framework for mobile edge computing networks
TLDR
This study applies federated learning to the demand prediction problem, to accurately forecast the more popular application types in the network, and reaches high accuracy levels on the predicted applications demand. Expand
Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence
TLDR
This article proposes an efficient and privacy-enhanced federated learning (PEFL) scheme for IAI that is noninteractive, and can prevent private data from being leaked even if multiple entities collude with each other. Expand
On the Relationship Between Inference and Data Privacy in Decentralized IoT Networks
  • Meng Sun, Wee Peng Tay
  • Computer Science, Mathematics
  • IEEE Transactions on Information Forensics and Security
  • 2020
TLDR
This work proposes an optimization framework in which both local differential privacy (data privacy) and information privacy (inference privacy) metrics are incorporated and introduces the concept of privacy implication (with vanishing budget) to study the relationships between these privacy metrics. Expand
VerifyNet: Secure and Verifiable Federated Learning
TLDR
VerifyNet is proposed, the first privacy-preserving and verifiable federated learning framework that claims that it is impossible that an adversary can deceive users by forging Proof, unless it can solve the NP-hard problem adopted in the model. Expand
EdgeSanitizer: Locally Differentially Private Deep Inference at the Edge for Mobile Data Analytics
TLDR
The theoretical analysis proves that EdgeSanitizer can provide provable privacy guarantees with a large improvement in utility and the experimental results demonstrate the robustness of the approach against sensitive inference, as well as its applicability on resource-constrained edge devices. Expand
Distributed Clustering in the Anonymized Space with Local Differential Privacy
TLDR
This paper extends the Bit Vector, a novel anonymization mechanism, and proposes kCluster algorithm that can be used for clustering in the anonymized space, and shows the modified encoding mechanism can be easily implemented in existing clustering algorithms that only rely on distance information, such as DBSCAN. Expand
Privacy Preserving Data Aggregation Scheme for Mobile Edge Computing Assisted IoT Applications
TLDR
The proposed privacy preserving data aggregation scheme not only guarantees data privacy of the TDs but also provides source authentication and integrity, and is very suitable for MEC assisted IoT applications. Expand
...
1
2
3
4
5
...