On Differential Privacy-Based Framework for Enhancing User Data Privacy in Mobile Edge Computing Environment

Sharma, Jhilakshi; Kim, Dong-Hyun; Ahyoung, Lee; Seo, Daehee

doi:10.1109/access.2021.3063603

Cited by 18 publications

(7 citation statements)

References 23 publications

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“…They used a generalized likelihood ratio test (GLRT) that was made for frequency-selective fading channels. This test laid the groundwork for later improvements in the literature [6], [7]. Notably, these improvements made the identification method better by adding things like power spectrum densities and channel-phase response.…”

Section: Related Workmentioning

confidence: 93%

Machine Learning and Enhanced Encryption for Edge Computing in IoT and Wireless Networks

Vaishali Raut , Prasanna Palsodkar , Mithun G Aush

2024

jes

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Machine Learning (ML) and better encryption methods is a key part of fixing the security and speed problems that Edge Computing causes in the Internet of Things (IoT) and Wireless Networks. The goal of this study is to improve the security of edge devices and networks so that critical data created and processed at the edge stays private and secure. Anomaly detection, threat identification, and adaptable security mechanisms depend on machine learning algorithms in a big way. These algorithms allow for proactive defenses against cyber dangers that are always changing. The proposed system used homomorphic encryption and quantum-resistant cryptography, to make data more private and secure. Even in edge devices with limited resources, these security methods keep data transfer and storage safe. The combination of machine learning and stronger encryption not only protects the IoT environment but also makes the best use of resources by changing security measures on the fly as threats change. This study adds to the development of safe and effective edge computing models, which helps IoT and wireless networks become more popular. The results can be used in many situations, from smart cities to industrial robotics. This makes sure that the advantages of edge computing can be enjoyed without putting the safety and privacy of the linked systems at risk.

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Section: Related Workmentioning

confidence: 93%

Machine Learning and Enhanced Encryption for Edge Computing in IoT and Wireless Networks

Vaishali Raut , Prasanna Palsodkar , Mithun G Aush

2024

jes

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“…Authors in [ 124 ] proposed a model based on differential privacy, called differential privacy fuzzy convolution neural network framework (DP-FCNN). First, they used the addition of noise to protect sensitive information by using a fuzzy CNN with a Laplace mechanism, then secured data storage, and encryption with a lightweight encryption algorithm named PICCOLO before uploading it to the cloud.…”

Section: Artificial Intelligence In Edge-based Iot Applications: Lite...mentioning

confidence: 99%

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

Bourechak

Zedadra

Kouahla

et al. 2023

Sensors

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Given its advantages in low latency, fast response, context-aware services, mobility, and privacy preservation, edge computing has emerged as the key support for intelligent applications and 5G/6G Internet of things (IoT) networks. This technology extends the cloud by providing intermediate services at the edge of the network and improving the quality of service for latency-sensitive applications. Many AI-based solutions with machine learning, deep learning, and swarm intelligence have exhibited the high potential to perform intelligent cognitive sensing, intelligent network management, big data analytics, and security enhancement for edge-based smart applications. Despite its many benefits, there are still concerns about the required capabilities of intelligent edge computing to deal with the computational complexity of machine learning techniques for big IoT data analytics. Resource constraints of edge computing, distributed computing, efficient orchestration, and synchronization of resources are all factors that require attention for quality of service improvement and cost-effective development of edge-based smart applications. In this context, this paper aims to explore the confluence of AI and edge in many application domains in order to leverage the potential of the existing research around these factors and identify new perspectives. The confluence of edge computing and AI improves the quality of user experience in emergency situations, such as in the Internet of vehicles, where critical inaccuracies or delays can lead to damage and accidents. These are the same factors that most studies have used to evaluate the success of an edge-based application. In this review, we first provide an in-depth analysis of the state of the art of AI in edge-based applications with a focus on eight application areas: smart agriculture, smart environment, smart grid, smart healthcare, smart industry, smart education, smart transportation, and security and privacy. Then, we present a qualitative comparison that emphasizes the main objective of the confluence, the roles and the use of artificial intelligence at the network edge, and the key enabling technologies for edge analytics. Then, open challenges, future research directions, and perspectives are identified and discussed. Finally, some conclusions are drawn.

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“…Definition 5. (Laplace mechanism) [12,13]. Given dataset D, there is a function f : D ⟶ R d , the sensitivity is Δf , and then the random algorithm MðDÞ = f ðDÞ + Y provides ε-differential privacy protection, where Y~LapðΔf /εÞ is the random noise and obeys the Laplace distribution with the scale parameter Δf /ε.…”

Section: Definitionsmentioning

confidence: 99%

Differential Privacy Location Protection Method Based on the Markov Model

Wang

Feng

et al. 2021

Wireless Communications and Mobile Computing

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Location-based services (LBS) have become an important research area with the rapid development of mobile Internet technology, GPS positioning technology, and the widespread application of smart phones and social networks. LBS can provide convenience and flexibility for the users’ daily life, but at the same time, it also brings security risks to the users’ privacy. Untrusted or malicious LBS servers can collect users’ location data through various ways and disclose it to the third party, thus causing users’ privacy leakage. In this paper, a differential privacy location protection method based on the Markov model for user’s location privacy is proposed. Firstly, the transition probability matrix between states of the n -order Markov model is used to predict the occurrence state and development trend of events; thereby, the user’s location is predicted, and then a location prediction algorithm based on the Markov model (LPAM) is proposed. Secondly, a location protection algorithm based on differential privacy (LPADP) is proposed, in which location privacy tree (LPT) is constructed according to the location data and the difficulty of retrieval, the two nodes with the largest predicted value of LPT are allocated with a reasonable privacy budget, and Laplace noise is added to protect location privacy. Theoretical analysis and experimental results show that the proposed method not only meets the requirements of differential privacy and protects location privacy effectively but also has high data availability and low time complexity.

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On Differential Privacy-Based Framework for Enhancing User Data Privacy in Mobile Edge Computing Environment

Cited by 18 publications

References 23 publications

Machine Learning and Enhanced Encryption for Edge Computing in IoT and Wireless Networks

Machine Learning and Enhanced Encryption for Edge Computing in IoT and Wireless Networks

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

Differential Privacy Location Protection Method Based on the Markov Model

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