The data-dispersal package

[Tags:lgpl, library, test]

Given a ByteString of length D, we encode the ByteString as a list of n Fragments, each containing a ByteString of length O(D/m). Then, each fragment could be stored on a separate machine to obtain fault-tolerance: Even if all but m of these machines crash, we can still reconstruct the original ByteString out of the remaining m fragments. Note that the total space requirement of the m fragments is m * O(D/m)=O(D), which is clearly space-optimal. The total space required for the n fragments is O((n/m)*D). Note that m and n can be chosen to be of the same order, so the asymptotic storage overhead for getting good fault-tolerance increases only by a constant factor.

GHCi Example:

> :m + Data.IDA
> let msg = Data.ByteString.Char8.pack "my really important data"
> let fragments = encode 5 15 msg
-- Now we could distributed the fragments on different sites to add some
-- fault-tolerance.
> let frags' = drop 5 $ take 10 fragments -- let's pretend that 10 machines crashed
-- Let's look at the 5 fragments that we have left:
> mapM_ (Prelude.putStrLn . show)  frags'
-- Space-efficiency: Note that the length of each of the 5 fragments is 5
-- and our original message has length 24.
> decode frags'
"my really important data"

Encrypted Fragments:

The module Data.IDA contains an information dispersal algorithm that produces space-optimal fragments. However, the knowledge of 1 or more fragments might allow an adversary to deduce some information about the original data. The module Crypto.IDA combines information dispersal with secret sharing: the knowledge of up to m-1 fragments does not leak any information about the original data.

This could be useful in scenarios where we need to store data at untrusted storage sites: To this end, we store one encrypted fragment at each site. If at most m-1 of these untrusted sites collude, they will still be unable to obtain any information about the original data. The added security comes at the price of a slightly increased fragment size (by an additional constant 32 bytes) and an additional overhead in the running time of the encoding/decoding process. The algorithm is fully described in module Crypto.IDA.


Suppose that we have N machines and encode our data as 2log(N) fragments with reconstruction threshold m = log(N). Let's assume that we store each fragment on a separate machine and each machine fails (independently) with probability at most 0.5.

This library is based on the following works:


Dependencies AES (>=0.2.9), array (>=, base (>=4.6 && <5), binary (>=, bytestring (>=, entropy (>=0.3.2), finite-field (>=0.8.0), matrix (>=, secret-sharing (>=, syb (>=0.4.0), vector (>= [details]
License LGPL-2.1
Copyright Peter Robinson 2014
Author Peter Robinson <>
Category Data, Cryptography
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Uploaded Sun Oct 5 17:24:55 UTC 2014 by PeterRobinson
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