Privacy-Preserving Deep Reinforcement Learning in Vehicle Ad Hoc Networks

Ahmed, Usman; Lin, Jerry Chun‐Wei; Srivastava, Gautam

doi:10.1109/mce.2021.3088408

Cited by 11 publications

(5 citation statements)

References 15 publications

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“…Ahmed et al [78] considered reinforcement learning of privacy-preserving in vehicle Adhoc networks. In this work, a deep reinforcement learning method is used to sensitize the private information for a given vehicle connected over Vehicle Adhoc networks.…”

Section: Summary and Discussionmentioning

confidence: 99%

New challenges in reinforcement learning: a survey of security and privacy

Lei

Shen

et al. 2022

Artif Intell Rev

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Reinforcement learning (RL) is one of the most important branches of AI. Due to its capacity for self-adaption and decision-making in dynamic environments, reinforcement learning has been widely applied in multiple areas, such as healthcare, data markets, autonomous driving, and robotics. However, some of these applications and systems have been shown to be vulnerable to security or privacy attacks, resulting in unreliable or unstable services. A large number of studies have focused on these security and privacy problems in reinforcement learning. However, few surveys have provided a systematic review and comparison of existing problems and state-of-the-art solutions to keep up with the pace of emerging threats. Accordingly, we herein present such a comprehensive review to explain and summarize the challenges associated with security and privacy in reinforcement learning from a new perspective, namely that of the Markov Decision Process (MDP). In this survey, we first introduce the key concepts related to this area. Next, we cover the security and privacy issues linked to the state, action, environment, and reward function of the MDP process, respectively. We further highlight the special characteristics of security and privacy methodologies related to reinforcement learning. Finally, we discuss the possible future research directions within this area.

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Section: Summary and Discussionmentioning

confidence: 99%

New challenges in reinforcement learning: a survey of security and privacy

Lei

Shen

et al. 2022

Artif Intell Rev

View full text Add to dashboard Cite

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“…To secure the sensitive information contained in big data, many researchers have devoted themselves to investigating various privacy protection solutions, such as threshold-based privacy protection [31] , [32] , [33] , differential privacy [34] , [35] , [36] , Simhash [37] , [38] , and so on. In addition, data sparsity is inevitable in the big data environment [39] , [40] , [41] .…”

Section: Related Literaturementioning

confidence: 99%

Time-aware sport goods sale prediction for healthcare with privacy-preservation

Zhou

Feng

2023

ISA Transactions

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“…Their proposed protocol is based on the block chain and PUF technology. Furthermore, Ahmed et al (Ahmed, Lin, & Srivastava, 2021b) propose a defense mechanism using deep reinforcement learning to secure the important information exchange over Vehicle Adhoc Networks. All of these mentioned studies point to the optimum protection mechanism for attacks in various domains.…”

Section: Related Workmentioning

confidence: 99%

Secure Gait Recognition-Based Smart Surveillance Systems Against Universal Adversarial Attacks

Bukhari

Yasmin

Gillani

et al. 2023

Journal of Database Management

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Currently, the internet of everything (IoE) enabled smart surveillance systems are widely used in various fields to prevent various forms of abnormal behaviors. The authors assess the vulnerability of surveillance systems based on human gait and suggest a defense strategy to secure them. Human gait recognition is a promising biometric technology, but one significantly hindered because of universal adversarial perturbation (UAP) that may trigger system failure. More specifically, in this research study, the authors emphasize on sample convolutional neural network (CNN) model design for gait recognition and assess its susceptibility to UAPs. The authors compute the perturbation as non-targeted UAPs, which trigger a model failure and lead to an inaccurate label to the input sample of a given subject. The findings show that a smart surveillance system based on human gait analysis is susceptible to UAPs, even if the norm of the generated noise is substantially less than the average norm of the images. Later, in the next stage, the authors illustrate a defense mechanism to design a secure surveillance system based on human gait.

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Privacy-Preserving Deep Reinforcement Learning in Vehicle Ad Hoc Networks

Cited by 11 publications

References 15 publications

New challenges in reinforcement learning: a survey of security and privacy

New challenges in reinforcement learning: a survey of security and privacy

Time-aware sport goods sale prediction for healthcare with privacy-preservation

Secure Gait Recognition-Based Smart Surveillance Systems Against Universal Adversarial Attacks

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