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Marius Drechsler 2024-08-20 17:19:09 +02:00
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@ -172,6 +172,8 @@ During every iteration of @alg:bach_1, we will store all weights $bold(h)$ used
The output of @alg:bach_1 is the vector of optimal weights $bold(h)_"opt"$. The output of @alg:bach_1 is the vector of optimal weights $bold(h)_"opt"$.
$bold(h)_"opt"$ can now be used to complete the enrollment phase and quantize the values $bold(z)_"opt"$. $bold(h)_"opt"$ can now be used to complete the enrollment phase and quantize the values $bold(z)_"opt"$.
To perform reconstruction, we can construct the same linear combination used during enrollment with the found helper-data and the new PUF readout measurements.
=== Maximum quantizing bound distance approximation === Maximum quantizing bound distance approximation
Instead of defining the optimal positions for $z$ with fixed values, we can also provide a more loose definition of $bold(cal(o))$. Instead of defining the optimal positions for $z$ with fixed values, we can also provide a more loose definition of $bold(cal(o))$.