1 Efficient Chains Of Unpredictable Numbers
2 =========================================
7 The Lightning Network wants a chain of (say 1 million) unguessable 256
8 bit values; we generate them and send them one at a time to a remote
9 node. We don't want the remote node to have to store all the values,
10 so it's better if they can derive them once they see them.
15 A simple system is a hash chain: we select a random seed value, the
16 hash it 1,000,000 times. This gives the first "random" number.
17 Hashed 999,999 times gives the second number, etc. ie:
22 This way the remote node needs only to remember the last R(N) it was
23 given, and it can calculate any R for N-1 or below.
25 However, this means we need to generate 1 million hashes up front, and
26 then do almost as many hashes to derive the next number. That's slow.
28 A More Complex Solution
29 -----------------------
31 Instead of a one-dimensional chain, we can use two dimensions: 1000
32 chains of 1000 values each. Indeed, we can set generate the "top" of
33 each chain much like we generated a single chain:
35 Chain 1000 Chain 999 Chain 998 ...........Chain 1
36 seed SHA256(C1000) SHA256(C999) ....... SHA256(C2)
38 Now, deriving chain 1000 from seed doesn't quite work, because it'll
39 look like this chain, so we flip the lower bit to generate the chain:
41 Chain 1000 Chain 999 Chain 998 ...........Chain 1
42 1000 seed^1 SHA256(C1000)^1 SHA256(C999)^1...... SHA256(C2)^1
43 999 SHA256(above) SHA256(above) SHA256(above) ..... SHA256(above)
44 998 SHA256(above) SHA256(above) SHA256(above) ..... SHA256(above)
47 Now, we can get the first value to give out (chain 1, position 1) with
48 999 hashes to get to chain 1, and 999 hashes to get to the end of the
49 chain. 2000 hashes is much better than the 999,999 hashes it would
50 have taken previously.
52 Why Stop at 2 Dimensions?
53 -------------------------
55 Indeed, the implementation uses 64 dimensions rather than 2, and a
56 chain length of 2 rather than 1000, giving a worst-case of 63 hashes
57 to derive any of 2^64 values. Each dimension flips a different bit of
58 the hash, to ensure the chains are distinct.
60 For simplicity, I'll explain what this looks like using 8 dimensions,
61 ie. 8 bits. The seed value always sits the maximum possible index, in
62 this case index 0xFF (b11111111).
64 To generate the hash for 0xFE (b11111110), we need to move down
65 dimension 0, so we flip bit 0 of the seed value, then hash it. To
66 generate the hash for 0xFD (b11111101) we need to move down dimension
67 1, so we flip bit 1 of the seed value, then hash it.
69 To reach 0xFC (b11111100) we need to move down dimension 1 then
70 dimension 0, in that order.
72 Spotting the pattern, it becomes easy to derive how to reach any value:
75 for bit in 7 6 5 4 3 2 1 0:
76 if bit not set in index:
80 Handling Partial Knowledge
81 --------------------------
83 How does the remote node, which doesn't know the seed value, derive
86 Once it knows the value for index 1, it can derive the value for index
87 0 by flipping bit 0 of the value and hashing it. In effect, it can
88 always derive a value for any index where it only needs to clear bits.
90 So, index 1 gives index 0, but index 2 doesn't yield index 1. When
91 index 3 comes along, it yields 2, 1, and 0.
93 How many hash values will we have to remember at once? The answer is
94 equal to the number of dimensions. It turns out that the worst case
95 for 8 dimensions is 254 (0b11111110), for which we will have to
96 remember the following indices:
107 127 lets us derive any hash value for index <= 127. Similarly, 191
108 lets us derive anything > 127 but <= 191. 254 lets us derive only
111 When we get index 255 this collapses, and we only need to remember
112 that one index to derive everything.
114 Rusty Russell <rusty@rustcorp.com.au>