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Which Drawbacks of Cloud Services Can Be Overlooked When Using Privacy-Preserving Machine Learning Techniques?

According to McKinsey, by 2030, the adoption of cloud technologies could contribute up to US$3 trillion in EBITDA across industries such as retail, pharmaceuticals, and energy.

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Nothing Personal, It’s Just Insurance

How can you offer the best policy without knowing anything personal about the client?

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About us

Skunkworks refers to teams that are given the freedom to operate without strict rules.
We discussed this and a couple of related topics with the Guardora's R&D team.

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Is Plug & Play possible in Privacy-Preserving Machine Learning?

Can privacy be conveniently packaged into a box?
Should PET teams collaborate closely?

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Smart Banks Learn from Their Mistakes, Wise Banks Learn from Federated Machine Learning

For an individual bank, the issue doesn't start with the United Nations' estimate that up to $2 trillion is laundered globally each year.

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Dataset Distillation

Training neural network models requires huge amounts of constantly updated data. Collecting, storing, processing, and transmitting such amounts results in enormous computation costs.

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Walls Have Ears, but Everything Else Belongs to the Internet of Things

Case Studies on Combining the Internet of Things, Machine Learning, and Confidential Computing.

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Swarm Intelligence and Privacy Enhancing Technologies

How can Privacy Enhancing Technologies help overcome the challenges in developing and applying swarm intelligence?

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Medical Confidentiality and Data Privacy in Machine Learning

Healthcare is one of the fields where machine learning and confidential computing are in high demand.

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