Enhanced Security and Privacy
The shift towards data processing at the edge has huge security implications. Sensitive data collected and analyzed on the edge must be protected from cyber-attacks and unauthorized access. In order to secure both data and devices at the edge, there should be secure booting; and encryption based on hardware; together with strong authentication protocols.
As a result, more privacy-sensitive approaches such as federated learning and homomorphic encryption will emerge to enable data analysis without sharing personal details. One can train machine learning models collaboratively over decentralized datasets using federated learning keeping individual data on edge devices. Homomorphic encryption ensures that computations can be performed on encrypted data thereby maintaining privacy even during analysis.
Therefore, there has been an upsurge in the need for new governance frameworks and regulations with respect to edge data that will help address these challenges and establish clear guidelines for responsible collection, storage, and usage of data at the edge.
Top 7 Trends in Edge Computing
In a world where data flows not only from, but within the very fabric of our surroundings. This is the promise of edge computing – an enabling technology that pushes the limits of data processing and decision-making closer to its source – the edge.
It allows devices at the front line to instantly analyze and act on data. It also paves the way for a new breed of real-time insights, self-governing operations by machines, as well as smart decision-making that impacts various industry players such as health, manufacturing, retail, and transportation sectors.
But it’s not like the edge is stagnant. Instead, it’s a shifting landscape driven by future-shaping cutting-edge trends. Therefore, pull up your socks as we take a deep dive into seven top trends set to change edge computing forever.
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