Research also reveals that sharingAImodels instead of raw data does not fully safeguard privacy, as certain attacks can still compromise sensitive training information. This paper reviewsEdgeIntelligence with a focus on privacy andsecurityissues, identifying critical challenges and vulnerabilities inedgeandcloud computing environments.
The decentralization implies thatAImodels are deployed across many devices, each potentially vulnerable. As a result,securitymeasures must be robustly implemented at eachedgenode to mitigate theriskof unauthorized access, tampering, or malicious exploitation, requiring inexpensive and scalable safeguards against various attacks.
The Internet of Things (IoT) has revolutionized modern life, yet its interconnected nature poses significantsecuritychallenges. This survey investigates threats andcountermeasuresassociated withIoTecosystems, categorizes threats targeting network infrastructure, devices, and datasecurity,andprovides a taxonomy to understand vulnerabilities. By analyzingIoTarchitectures—including ...
Thesecurityrisksassociated with gen-erativeAIinIoTare multifaceted, ranging from data privacy breaches to the potential misuse ofAItechnologies. Theserisksnot only pose threats to the integrity and reliability ofIoTsystems but also to the trust and safety of users.
This study explores the landscape ofsecurityinedgecomputing andIoT, examining key threats, existingsecurityframeworks, and best practices for mitigatingrisks. By providing a detailed analysis of current approaches and future directions, this article aims to offer comprehensive insights for securingedgeandIoTenvironments.
This study confirms that hybridEdge-CloudAIsecuritymodels are a strong, scalable, and privacy-sensitive option to securing distributedIoT-driven cloud governments. The high development in the number of Internet of Things (IoT) devices and distributed cloud environments have presented challengingsecurityissues within contemporaryEdge-Cloud environments. This paper is a suggested ...
