Category: Confidential Computing

COP 26 Glasgow
Confidential Computing

The Energy Efficiency of Confidential Computing

With COP26 Glasgow taking place today, here are some thoughts on confidential computing and environmental concerns.

With cloud computing being in high demand, companies are competing on resources of electricity, space, and ventilation. This is happening as the demand for better performance is constantly on the rise, heavy processing is taking place, and algorithmic needs are ever-changing.

On an environmental level – this all translates to high levels of energy consumption, which causes carbon emission.

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Edge computing & vehicles
Confidential Computing

Living Safely on the Edge

A Gartner report from 2020 described Edge Computing as “part of a distributed computing topology in which information processing is located close to the edge — where things and people produce or consume that information.”

The International Data Corporation predicts that more than half of new enterprise infrastructure will be at the edge by 2023, and worldwide spending on edge computing will reach $250 billion by 2024. According to Gartner Inc.’s research, data generated at the edge will increase from 10% today to 75% by 2025.

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HealthCare & AI Protection by HUB Security
Artificial Intelligence

AI in Healthcare Security

HUB Security’s hardware solution allows a safe environment for machine learning and artificial intelligence processes to take place, while any concern for unwanted visibility between different models. By using Confidential Computing methods we allow for effective collaborative machine learning to take place, thus enabling previously unattainable ventures, in a secure environment.

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Confidential computing diagram
Artificial Intelligence

Cutting Edge

For the experienced reader, edge computing should not be too hard to comprehend. In July 1969, Apollo 11 landed on the moon, in large part

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Valerii Babushkin
Artificial Intelligence

AI & Data Privacy with Valerii Babushkin

Towards our online summit on June 10th, AI and Data Privacy we set down for an interview with Valerii Babushkin, WhatsApp User Data Privacy Tech Lead, who will be joining the event.

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Security threats facing AI and ML
Artificial Intelligence

Top 5 Security Threats Facing Artificial Intelligence and Machine Learning

An emerging space within AI is the need to share and access more big data from semi trusting parties in order to achieve better models and insights. A good example is multiple healthcare providers sharing images and their interpretation in order to create an AI model to detect anomalies on its own. The more images the better algorithm. This scenario requires the model to access data from all healthcare providers, but assure that images cannot be accessed by each individual healthcare provider to another.

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