Add SIS Estimate Functionality #96
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malb
reviewed
Feb 1, 2024
docs/algorithms/sis-lattice.rst
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| params = SIS.Parameters(n=113, q=2048, length_bound=512, norm=2) | ||
| params | ||
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| The simplest (and quickest to estimate) model is solving for the SIS instance with a euclidian norm length bound and assuming the Gaussian heuristic [CheNgu12]_.Then, we can solve for the required root hermite factor [EC:GamNgu08]_ that will guarantee BKZ outputs a short enough vector:: |
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Why are we referencing CheNgu12 here?
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Fair enough. Couldn't exactly find a better reference for the Gaussian heuristic. Removed.
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Ah, sorry, I misread, yes, this is fine. Sorry for the noise
docs/algorithms/sis-lattice.rst
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| SIS.lattice(params) | ||
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| The exact reduction shape model doesn't matter when using euclidian norm bounds, as the required block size is calculated directly from the length bound. |
docs/algorithms/sis-lattice.rst
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| SIS.lattice(params.updated(length_bound=70), red_shape_model=Simulator.CN11) | ||
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| Another option is to simulate a rerandomization of the basis, such that the q-vectors are *forgotten*. This results in the ``LGSA`` simulator, where the short, unit vectors are still present in the basis. See Figure 12 in the dilithium submission for an example.We can then improve on this result by first preprocessing the basis with block size β followed by a single SVP call in dimension η [RSA:LiuNgu13]_. We call this the BDD approach since this is essentially the same strategy as preprocessing a basis and then running a CVP solver:: |
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This BDD stuff seems a leftover from somewhere else?
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It was. Forgot to delete. Resolved.
estimator/sis_lattice.py
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| **kwds, | ||
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| """ | ||
| This function optimizes costs for a fixed guessing dimension ζ. |
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Guessing dimension might be a bit misleading?
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Changed to make a bit more descriptive.
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Wooohoo! |
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Resolves #25
Adds a new, separate API for SIS estimation specifically, alongside a new SISParameters data class. Initial support is only for lattice attacks on SIS, so only an sis_lattice attack file is included. Outputs for SIS with an Infinity norm length bound are tuned to match the Dilithium NIST Round 3 specification https://pq-crystals.org/dilithium/data/dilithium-specification-round3-20210208.pdf when using the new basis shape estimator. Schemes.py has been updated to include unforgeability parameters for Dilithium and secret key recovery plus unforgeability parameters for Falcon.