Filters








29,904 Hits in 9.5 sec

Privacy and Security Issues in Deep Learning: A Survey

Ximeng Liu, Lehui Xie, Yaopeng Wang, Jian Zou, Jinbo Xiong, Zuobin Ying, Athanasios V. Vasilakos
2020 IEEE Access  
Finally, we discuss current challenges and open problems regarding privacy and security issues in DL.  ...  Deep Learning (DL) algorithms based on artificial neural networks have achieved remarkable success and are being extensively applied in a variety of application domains, ranging from image classification  ...  A. MOTIVATION Privacy and security issues in DL have been becoming a hot topic in recent years. In this paper, we present a comprehensive survey on the privacy and security issues of DL.  ... 
doi:10.1109/access.2020.3045078 fatcat:kbpqgmbg4raerc6txivacpgcia

Security and Privacy Issues in Deep Learning [article]

Ho Bae, Jaehee Jang, Dahuin Jung, Hyemi Jang, Heonseok Ha, Hyungyu Lee, Sungroh Yoon
2021 arXiv   pre-print
In this paper, we describe the notions of some of methods, e.g., homomorphic encryption, and review their advantages and challenges when implemented in deep-learning models.  ...  To promote secure and private artificial intelligence (SPAI), we review studies on the model security and data privacy of DNNs.  ...  We review recent research on privacy and security issues associated with DL in several domains.  ... 
arXiv:1807.11655v4 fatcat:k7mizsqgrfhltktu6pf5htlmy4

A survey of security and privacy issues in the Internet of Things from the layered context [article]

Samundra Deep, Xi Zheng, Alireza Jolfaei, Dongjin Yu, Pouya Ostovari, Ali Kashif Bashir
2020 arXiv   pre-print
The purpose of this paper is to highlight the security and privacy issues in IoT systems.  ...  To this effect, the paper examines the security issues at each layer in the IoT protocol stack, identifies the underlying challenges and key security requirements and provides a brief overview of existing  ...  Our survey paper reviews and analyses the security and privacy issues in IoT. The rest of the paper is as follows: Section 2 contains the background of IoT.  ... 
arXiv:1903.00846v2 fatcat:jfwwbyw2cfaozk3uygz3ulc22a

Privacy in Deep Learning: A Survey [article]

Fatemehsadat Mireshghallah, Mohammadkazem Taram, Praneeth Vepakomma, Abhishek Singh, Ramesh Raskar, Hadi Esmaeilzadeh
2020 arXiv   pre-print
In this survey, we review the privacy concerns brought by deep learning, and the mitigating techniques introduced to tackle these issues.  ...  We also show that there is a gap in the literature regarding test-time inference privacy, and propose possible future research directions.  ...  Existing Threats In this section, we map the space of existing threats against privacy in deep learning and machine learning in general.  ... 
arXiv:2004.12254v5 fatcat:4w63htwzafhxxel2oq3z3pwwya

Privacy-Preserving Deep Learning on Machine Learning as a Service - A Comprehensive Survey

Harry Chandra Tanuwidjaja, Rakyong Choi, Seunggeun Baek, Kwangjo Kim
2020 IEEE Access  
[61] to address the privacy issue in Machine Learning as a Service (MLaaS).  ...  In MLaaS, the overhead issues occur during the HE process, deep learning training (including inferencing), and data perturbation.  ... 
doi:10.1109/access.2020.3023084 fatcat:inxcd6sbhfewtkm4q3krnkx4oe

Federated Learning for Smart Healthcare: A Survey [article]

Dinh C. Nguyen, Quoc-Viet Pham, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne, Zihuai Lin, Octavia A. Dobre, Won-Joo Hwang
2021 arXiv   pre-print
Accordingly, we provide a comprehensive survey on the use of FL in smart healthcare.  ...  The recent FL designs for smart healthcare are then discussed, ranging from resource-aware FL, secure and privacy-aware FL to incentive FL and personalized FL.  ...  ✗ ✗ None A review on the security and privacy issues in FL systems. [14] FL in edge networks ✓ ✗ ✓ ✗ None A survey on the integration of FL in mobile edge networks. [15] FL for IoT ✓ ✓ ✓ ✗ None A survey  ... 
arXiv:2111.08834v1 fatcat:jmex4e25rbgy3bk67iolrj4uee

Machine Learning for Security in Vehicular Networks: A Comprehensive Survey [article]

Anum Talpur, Mohan Gurusamy
2021 arXiv   pre-print
In this paper, we present a comprehensive survey of ML-based techniques for different security issues in vehicular networks.  ...  We propose a taxonomy of security attacks in vehicular networks and discuss various security challenges and requirements.  ...  , Approaches, and Open Issues Security Attacks IoT Limited [28] 2020 A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security Security Threats Types and Threats  ... 
arXiv:2105.15035v2 fatcat:5z6aqlvosjgf3o3amts3k6toxu

From Distributed Machine Learning to Federated Learning: A Survey [article]

Ji Liu, Jizhou Huang, Yang Zhou, Xuhong Li, Shilei Ji, Haoyi Xiong, Dejing Dou
2021 arXiv   pre-print
ensuring data security and data privacy.  ...  In this paper, we provide a comprehensive survey of existing works for federated learning. We propose a functional architecture of federated learning systems and a taxonomy of related techniques.  ...  Some surveys [70, 91, 104] focus on the data privacy and security of FL.  ... 
arXiv:2104.14362v2 fatcat:25fdci3vhjdgznxpgofylxsj7i

Blockchain-based Federated Learning: A Comprehensive Survey [article]

Zhilin Wang, Qin Hu
2021 arXiv   pre-print
However, issues of privacy and scalability will constrain the development of machine learning.  ...  In this paper, we conduct a comprehensive survey of the literature on blockchained FL (BCFL).  ...  [20] surveys the research related to the privacy issues of FL, illustrating several attacks which will lead to the leakage of data privacy, e.g., membership inference attack and GAN-based(a deep learning  ... 
arXiv:2110.02182v1 fatcat:sm2mtftvq5fodgkfdhcan55n3q

Machine Learning at the Network Edge: A Survey [article]

M.G. Sarwar Murshed, Christopher Murphy, Daqing Hou, Nazar Khan, Ganesh Ananthanarayanan, Faraz Hussain
2021 arXiv   pre-print
A typical solution involves offloading data to external computing systems (such as cloud servers) for further processing but this worsens latency, leads to increased communication costs, and adds to privacy  ...  , frameworks, and hardware used in successful applications of intelligent edge systems.  ...  An overview of issues in XAI can be found in the survey by Adadi and Berrada [1] .  ... 
arXiv:1908.00080v4 fatcat:mw4lwwvzf5gupjr6pgdgnabeuu

No Peek: A Survey of private distributed deep learning [article]

Praneeth Vepakomma, Tristan Swedish, Ramesh Raskar, Otkrist Gupta, Abhimanyu Dubey
2018 arXiv   pre-print
The distributed deep learning methods of federated learning, split learning and large batch stochastic gradient descent are compared in addition to private and secure approaches of differential privacy  ...  We survey distributed deep learning models for training or inference without accessing raw data from clients.  ...  Table 1 : 1 This is a survey of distributed deep learning methods with decreasing levels of leakage from distributed NN to splitNN.  ... 
arXiv:1812.03288v1 fatcat:7w3oheeljrgejit6dsj54ay5nq

Machine Learning Systems for Intelligent Services in the IoT: A Survey [article]

Wiebke Toussaint, Aaron Yi Ding
2020 arXiv   pre-print
With a multi-layered framework to classify and illuminate system design choices, this survey exposes fundamental concerns of developing and deploying ML systems in the rising cloud-edge-device continuum  ...  Machine learning (ML) technologies are emerging in the Internet of Things (IoT) to provision intelligent services.  ...  Network security issues are not unique to cloud-based IoT systems.  ... 
arXiv:2006.04950v3 fatcat:xrjcioqkrrhpvgmwmutiajgfbe

A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection [article]

Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Yuan Li, Xu Liu, Bingsheng He
2021 arXiv   pre-print
In this survey, we conduct a comprehensive review on federated learning systems.  ...  Federated learning has been a hot research topic in enabling the collaborative training of machine learning models among different organizations under the privacy restrictions.  ...  Acknowledgement This work is supported by a MoE AcRF Tier 1 grant (T1 251RES1824), an SenseTime Young Scholars Research Fund, and a MOE Tier 2 grant (MOE2017-T2-1-122) in Singapore.  ... 
arXiv:1907.09693v6 fatcat:d3l2l664mjdfrjgyok43pfxnvq

Secure and Robust Machine Learning for Healthcare: A Survey

Adnan Qayyum, Junaid Qadir, Muhammad Bilal, Ala Al Fuqaha
2020 IEEE Reviews in Biomedical Engineering  
the myriad security and privacy issues involved), especially in light of recent results that have shown that ML/DL are vulnerable to adversarial attacks.  ...  Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction  ...  Different potential solutions to ensure secure and privacy-preserving ML are discussed in Section IV and various open research issues are outlined in Section V.  ... 
doi:10.1109/rbme.2020.3013489 pmid:32746371 fatcat:wd2flezcjng4jjsn46t24c5yb4

Federated Learning for Internet of Things: A Comprehensive Survey [article]

Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, H. Vincent Poor
2021 arXiv   pre-print
, and IoT privacy and security.  ...  In this article, we provide a comprehensive survey of the emerging applications of FL in IoT networks, beginning from an introduction to the recent advances in FL and IoT to a discussion of their integration  ...  FL-based Techniques for Privacy and Security in IoT Services and Networks In IoT networks, security and privacy remain huge issues for IoT devices, which introduce a whole new degree of attacks and privacy  ... 
arXiv:2104.07914v1 fatcat:b5wsrfcbynel7jqdxpfw4ftwh4
« Previous Showing results 1 — 15 out of 29,904 results