data-dispersal: Space-efficient and privacy-preserving data dispersal algorithms.
Given a ByteString of length
D, we encode the ByteString as a list of
Fragments, each containing a ByteString
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
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
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.
> :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' (6,[273,771,899,737,285]) (7,[289,939,612,285,936]) (8,[424,781,1001,322,788]) (9,[143,657,790,157,423]) (10,[314,674,418,888,423]) -- 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"
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.
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
with reconstruction threshold m =
Let's assume that we store each fragment on a separate machine and each
machine fails (independently) with probability at most 0.5.
What is the probability of our data being safe?
Pr[ at most n-m machines crash ] >= 1-0.5^(log(N)) = 1-N^(-1).
What is the overhead in terms of space that we pay for this level of fault-tolerance? We have n fragments, each of size
O(D/m), so the total space is
O(n D/ m) = 2D.In other words, we can guarantee that the data survives with high probability by increasing the required space by a constant factor.
This library is based on the following works:
"Efficient Dispersal of Information for Security, Load Balancing, and Fault Tolerance", by Michael O. Rabin, JACM 1989.
"How to share a secret." by Adi Shamir. In Communications of the ACM 22 (11): 612–613, 1979.
"Secret Sharing Made Short" Hugo Krawczyk. CRYPTO 1993: 136-146
|Versions [RSS]||22.214.171.124, 126.96.36.199, 188.8.131.52|
|Dependencies||AES (>=0.2.9), array (>=0.4.0.1), base (>=4.6 && <5), binary (>=0.7.2.1), bytestring (>=0.10.0.2), entropy (>=0.3.2), finite-field (>=0.8.0), matrix (>=0.3.4.0), secret-sharing (>=184.108.40.206), syb (>=0.4.0), vector (>=0.10.11.0) [details]|
|Copyright||Peter Robinson 2014|
|Author||Peter Robinson <firstname.lastname@example.org>|
|Uploaded||by PeterRobinson at 2014-10-05T17:24:55Z|
|Downloads||2683 total (11 in the last 30 days)|
|Rating||(no votes yet) [estimated by Bayesian average]|
|Status||Docs uploaded by user
Build status unknown [no reports yet]