Infosys Blogs
Infosys experts discuss the key drivers of business transformation and accelerating innovation.
Differential Privacy – A milestone in data privacy
In case of statistical databases like big data, it is crucial to protect sensitive information that may or may not be individual-specific. Here comes differential privacy for rescue. It ensures the data is not compromised, and at the same time, maximizes data accuracy.
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In case of statistical databases like big data, it is crucial to protect sensitive information that may or may not be individual-specific. Here comes differential privacy for rescue. It ensures the data is not compromised, and at the same time, maximizes data accuracy.
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Differential Privacy: The Privacy Guarantee
Differential Privacy (DP) provides robust data protection, which is not possible with trivial data privacy methods. But we must make sure that DP meets legal requirements and can identify privacy loss parameters based on certain regulations.
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Differential Privacy (DP) provides robust data protection, which is not possible with trivial data privacy methods. But we must make sure that DP meets legal requirements and can identify privacy loss parameters based on certain regulations.
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What exactly is quantum management key
This is the age of quantum computing. The computers are now becoming faster and runs the risk of breaking a highly complex key within days or hours. So, the only way to tackle this is quantum key management.
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This is the age of quantum computing. The computers are now becoming faster and runs the risk of breaking a highly complex key within days or hours. So, the only way to tackle this is quantum key management.
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Learning to Trust Synthetic Data…
There has been a growing demand in today's AI revolution to generate synthetic data. Large volume of data is required to train and build AI/ML models in order to solve use cases in real world. The only way to run the complex simulations is by Contextual Synthetic data generation.
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There has been a growing demand in today's AI revolution to generate synthetic data. Large volume of data is required to train and build AI/ML models in order to solve use cases in real world. The only way to run the complex simulations is by Contextual Synthetic data generation.
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