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  <title>Open Challenge - Privacy-Preserving AI Computing</title>
  <meta name="description" content="Open Research and Engineering Challenges in Privacy-Preserving AI.">

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          <section id="research-challenge">
            <h2>Open Research Challenge</h2>
            <ol>
              <li>
                <strong>Basis Conversion (BConv)</strong><br>
                involves two different sets of moduli in the experiments, which currently utilizes Barrett Reduction
                because Montgomery reduction would require bringing all data from the Montogmery domain of the old
                modulus back to the raw domain and then convert into the Montgomery domain of the new modulus. The chain
                overhead of domain conversion with actual Montgomery reduction makes the overhead higher than the pure
                Barrett reduction.
              </li>
              <li>
                <strong>Automorphism</strong><br>
                Automorphism requires a shuffling at the degree dimension, how to break such shuffling into a sequence
                of two matrix
                multiplication so that they could be fused into the previous computation remains an open research
                question.
              </li>
              <li>
                <strong>Automated Scheduling Optimization</strong><br>
                How to automatically search the optimal scheduling of HE kernels for different security parameters?
                Specifically, how to schedule the kernel-level operations such that the majority of off-chip memory
                access could be hidden behind computation?
              </li>
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            <h2>Open Engineering Challenge</h2>
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              <li>
                Implement rational rescaling to allow more multiplication depth.
              </li>
            </ol>
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