Towards the Science of Security and Privacy in Machine Learning

Why Machine Learning Needs Better Security and Privacy

Every day more apps and gadgets use machine learning to guess, decide, or help us, and that feels like magic.
But the same smart models can be tricked, exposed, or used the wrong way, and not everyone realizes it.
Researchers found new kinds of vulnerabilities that let people change outcomes or see private info, sometimes without leaving a trace.
Fixes exist, yet they often trade off speed or accuracy, so choosing what to protect is hard.
Some solutions make models stronger but slower, others hide data but make answers less clear.
It means designers must pick what matters for each product or place where the system runs.
This is about more than code, it touches trust and everyday safety.
If a self-driving car or health app fails because of a trick, stakes are high.
We need simple checks, honest testing, and better ways to keep user info safe while keeping systems useful.
The path forward is messy, but with care and smart choices we can make AI tools both helpful and safer for everyone.

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Towards the Science of Security and Privacy in Machine Learning

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