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Are there any relevant computationally intensive quantitative finance problems that could be outsourced to a trustless grid? By a trustless grid I mean that you cannot trust it with the access to your sensitive data (e.g. open positions).

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Would encryption as a remedy qualify? –  vonjd Jul 8 '13 at 14:12
    
@vonjd Sure, if you can come up with homomorphic encryption scheme. But this is usually an extremely tough problem. That's why I'm curious if there is something that doesn't require one to leak his or her private data but still is of practical interest. –  Alexey Kalmykov Jul 8 '13 at 14:54
    
Calculating option greeks would be a possibility? However, using proprietary algorithms in the trustless environment seems impossible unless you can split it in a public and a private part (but I doubt this set-up has a practical use case). –  Bob Jansen Jul 8 '13 at 18:09
    
You could throw sub-algorithms at it, what I mean with that is that you do not expose any proprietary part of your main algorithm but you outsource inputs to your algorithm that are computationally intensive such as complex matrix calculations that can potentially be split-up, or divide-and-conquer algorithms, basically stuff that lies in the public domain knowledge but would benefit from distributed grid or cloud computing. –  Matt Wolf Jul 9 '13 at 0:08
    
A solution I've seen in practice is to aks multiple computation where only one is the true data... the problem was to build credible fake datas. –  lmorin Jul 10 '13 at 9:54
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