Added learning objectives

privacy_security.qmd

@@ -19,7 +19,23 @@ unlocking the benefits of AI.
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 ## Learning Objectives
 
-* coming soon.
+* Understand key ML privacy and security risks like data leaks, model theft, adversarial attacks, bias, and unintended data access.
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+* Learn from historical hardware and embedded systems security incidents.
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+* Identify threats to ML models like data poisoning, model extraction, membership inference, and adversarial examples.
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+* Recognize hardware security threats to embedded ML spanning hardware bugs, physical attacks, side channels, counterfeit components, etc.
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+* Explore embedded ML defenses like trusted execution environments, secure boot, physical unclonable functions, and hardware security modules.
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+* Discuss privacy issues in handling sensitive user data with embedded ML, including regulations.
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+* Learn privacy-preserving ML techniques like differential privacy, federated learning, homomorphic encryption, and synthetic data generation.
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+* Understand tradeoffs between privacy, accuracy, efficiency, threat models, and trust assumptions.
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+* Recognize the need for a cross-layer perspective spanning electrical, firmware, software, and physical design when securing embedded ML devices.
 
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@@ -2925,3 +2941,8 @@ techniques and their pros and cons:
 
 
 ## Conclusion
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+Machine learning hardware security is a critical concern as embedded ML systems are increasingly deployed in safety-critical domains like medical devices, industrial controls, and autonomous vehicles. We have explored various threats spanning hardware bugs, physical attacks, side channels, supply chain risks and more. Defenses like trusted execution environments, secure boot, PUFs, and hardware security modules provide multilayer protection tailored for resource-constrained embedded devices. 
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+However, continual vigilance is essential to track emerging attack vectors and address potential vulnerabilities through secure engineering practices across the hardware lifecycle. As ML and embedded ML spreads, maintaining rigorous security foundations that match the field's accelerating pace of innovation remains imperative.
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