Parallel Mixing

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Parallel Mixing

• Philippe Golle, PARC
• Ari Juels, RSA Labs

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Anonymous Channel
Alice
Charlie
Bob
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What are Anonymous Channels Useful for?

• They underlie most privacy applications
• Anonymous elections
• Anonymous email
• Anonymous payments
• Anonymous Web browsing
• Censorship resistant publication

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Implementation Mix Network
Outputs
Inputs
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Mix Network
Outputs
Inputs
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One honest server guarantees privacy
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A Look Under the Hood

• Sealing an envelope public key encryption
• Decryption key is shared among mix servers
• Opening an envelope joint decryption
• Requires cooperation of a quorum of servers
• Mixing envelopes re-encryption
• We use a randomized encryption scheme
• many (2160) different ways to encrypt a message
• Re-encryption create a new ciphertext that
  decrypts to the same message
• Message is unchanged
• Ciphertext is unrecognizable
• Re-encryption is a public key operation

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Computational Cost

• Cost of mixing
• Dominated by re-encryption
• Re-encryption 2 modular exponentiations per
  input
• Assume n inputs and k servers
• Cost per server O(n)
• Assume sequential mixing
• Total mixing time is O(k.n)
• Can we decrease the total mixing time?
• Most of the mix servers are idle most of the time
• Idea parallelize the mixing!

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Parallel Mixing (1st Try)
Round 3
Round 2
Round 1
Outputs
Inputs
Batch 1
Batch 3
Batch 1
Batch 2
Batch 1
Batch 2
Batch 3
Batch 3
Batch 2
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Parallel Mixing (1st Try)

• Assume n inputs and k servers
• Divide inputs into k batches of size n/k
• Every server mixes every batch (in parallel)
• Computational cost
• Per server k. (n/k) n (as before)
• Total cost k. n kn (as before)
• Total mixing time k.(n/k) n (instead
  of kn)
• We cut the total mixing time by a factor of k
• But anonymity set is n/k instead of n
• Inputs are mixed within a batch
• There is no mixing between batches

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Building Block Rotation
Round i1
Round i
Batch 1
Batch 1
Rotation Each server passes its batch on to
the next server in round robin fashion
Batch 2
Batch 2
Batch 3
Batch 3
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Building Block Distribution
Round i1
Round i
Distribution Each server splits its batch and
gives one piece to every other server.
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Parallel Mixing Protocol

• Parameters
• n inputs
• k mix servers
• Adversary controls at most k servers (e.g.
  kk-1)

• k rounds of mixing rotation
• One distribution
• k rounds of mixing rotation

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Example ( k5, k 3)
Rotation
Mixing
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Example ( k5, k 3)
Mixing
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Example ( k5, k 3)
Mixing
Rotation
Distribution
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Parallel Mixing

• Protocol
• Divide inputs into k batches of size n/k
• k rounds of mixing and rotation (kltk)
• Distribution
• k rounds of mixing and rotation
• Computational cost
• Per server 2(k1)n/k 2n
• Total cost 2(k1)n 2kn
• Total mixing time 2(k1)n/k 2n
• Total mixing time divided by k2/2(k1) k/2
• Anonymity set of size n
• Cost per server is at most doubled

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Anonymity Set

• Recall that the adversary A may
• Control up to k mix servers
• Submit up to a fraction a of the n inputs
• Let p0 be an input (not submitted by A). We
  compute the probability
• that input p0 became output p1, in the view of
  A.
• Ideally,

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Anonymity Set
Outputs
Inputs
Distribution
p0
n/k
n/k
p1
Batch B0
Batch B1
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Anonymity Set

• Adversary controls no input
• Adversary controls a fraction a of the inputs

(assuming uniform distribution)
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Optimality

• Our construction has nearly optimal total mixing
  time 2(k1)n/k
• Proposition Let A be an adversary who controls
  kltk servers. Any mixnet with anonymity gt1 with
  respect to A must have total mixing time at least
  (k1)n/k.
• Proposition Let A be an adversary who controls
  kk-1 servers. Any mixnet with anonymity gt1 with
  respect to A must have total mixing time at least
  2n.

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Conclusion

• Our protocol reduces total mixing time from O(kn)
  to O(n)
• This is optimal within a factor of 2
• Open problem exact optimality?
• Questions?