Agree that setting H(0, 0) = 0 is an optimization!
Another thing I am thinking about is, is there an ultra-simple hack that can allow us to avoid having to do a hash call for H(x, 0) or H(0, x) as well? Unfortunately it seems like you can’t do it at least with the same domain (32 byte values) as the hash function, because if the function is invertible, then defining H^{-1}_{0L}(x) such that H_{0L}^{-1}(x) = y implies H(0, y) = x (and similarly for H_{0R}^{-1} for the 0 in the right position), then given any a = H(b, c), a = H(0, H_{0L}^{-1}(a)) = H(H_{0R}^{-1}(a), 0).
If there isn’t I don’t think that’s a big deal; hash functions are quite cheap and fast these days, so doing 256 hashes instead of 32 isn’t too big a deal, especially since it’s only needed for writes and not reads (and for multi-branch updates the work is parallelizable!), but something really clean and simple would be nice.
Can’t you replace subtrees which only contain one element by the element itself? You don’t get the exact same root hash as the equivalent SMT but it seems like you can still do correct inclusion and exclusion proofs into this tree.
Is it equivalent? Suppose the tree contains one element x; my suggestion is for the root to be H(x) but if H(0,0) = 0 is the only constraint then it seems like the root is H(0, something) or H(something, 0), which is necessarily different from H(x).
I was speaking in terms of an SMT that commits to unordered sets, in which case it never makes sense to place the same value at different paths. Here is one way to do it for a key-value mapping.
Let the key-value map be \{(k_i, v_i) | i \in I\} where and i \ne j \implies k_i \ne k_j). Let H be keccak-256. Create a complete binary merkle tree of depth 256 and store (k_i, v_i) at the H(k_i)-th leaf from the left. Replace each subtree which contains exactly one non-empty leaf with the leaf itself. This results in a binary tree that is not complete. Replace empty leaf with 0 and each non-empty leaf x with (1, x). Replace every non-leaf node by the hash of its two children, forming a merkle tree.
An inclusion proof that k maps to v is a merkle inclusion proof of (1, k, v), whose path is some prefix of H(k), i.e., an integer t \le 256, a path p \in \{0,1\}^{t} and t intermediate nodes of type bytes32; the checker verifies this proof by starting with the value (1, k, v) hashing with the provided intermediate nodes either on the left or right as directed by p, and comparing it to the root. An exclusion proof is either a merkle inclusion proof of 0 or an inclusion proof of (1, k', v') whose path is some prefix of k and where k' \ne k.
Hi everyone,
I’d like to share 2 implementations that I hope you might find useful as they seem similar to ideas expressed above : a standard SMT and a modified SMT of height log(N)
https://github.com/aergoio/SMT
This is a standard binary SMT implementation of height 256 with the following characteristics :
Node batching (1 db transaction loads 30 nodes : ie a subtree of height 4 with 16 leaves). Batch nodes are loaded into an array and can be concurrently updated.
Reduced data storage (if a subtree contains only 1 key then the branch is not stored and the root points to the KV pair directly
Parallel update of multiple sorted keys
https://github.com/aergoio/aergo/tree/master/pkg/trie
This implementation modifies the SMT in the following way : the value of a key is not stored at height 0 but instead at the highest subtree containing only that key. The leaf node of keys is [key, value, height].
The benefit here is that the tree height is log(N) and updating a key requires log(N) hashing operations (256 hashing operations becomes too slow if the tree is used to update thousands of keys/sec).
It also has node batching and parallel updates like the standard SMT implementation.
The size of a proof of inclusion and non-inclusion is log(N).
A proof of non-inclusion can be of 2 types :
A proof that another key’s leaf node is already on the path of the non-included key
Or a proof that an empty subtree is on the path of the non-included key
When putting values into the tree, a value v is replaced by (``", v). This hash function is collision-resistant, which you can prove piecewise and then finish by showing domain independence (cases 2 and 3 clearly cannot collide with each other, cases 2/3 cannot collide with case 4 because they give outputs longer than 32 bytes,and case 1 cannot collide with anything because cases 2 and 3 can’t give a 0 because by preimage resistance finding a value that hashes to 0 is infeasible.
The only argument I have against it is that it’s somewhat uglier, because the values aren’t cleanly 32 bytes anymore, instead they go up to 64 bytes, and because we need to deal with encodings for arbitrary-bit-length strings. I guess it depends on just how expensive hashes are.
Agreed, although the update algorithm is different because when adding a key in the aergo trie implementation if an empty subtree is reached, a Leaf = Hash(key,value,height) is created and there is no need to iterate the branch. key and value are stored in place of the imaginary left and right subtree roots of the Leaf node for easy serialization
The readme has diagrams
Is it different? The result of iterating the branch is simple, it’s just (binarystring(key), value). So I suppose you just get an additional shortcut over doing ~256-log(N) loops.
@vbuterin I understand that this would allow for the mekle proof to be of variable number of nodes.
So if i use a Bulletproofs circuit to prove knowledge of a leaf in the tree, the proof size can give an approximate idea of where the leaf can be in tree. Am i wrong on this?
Actually this makes it so that you can have a Merkle proof always have the exact same number of nodes (256), using compression at a higher layer to bring the scheme back to O(log(N)) efficiency.
@vbuterin I think you are referring to using compress_proof and decompress_proof on a naive sparse merkle tree. I was referring to implementations in new_bintrie_optimized.py and new_bintrie_hex.py.
Those implementations are just changes to how the data is stored in the database, they don’t actually change the verification that gets executed. new_bintrie_optimized.py, new_bintrie_hex.py and the simple SMT are all different algorithms to implement the exact same thing. So in either case you’d be doing a bulletproof over 256 hashes.
The number of proof nodes is quite different in case of simple SMT and new_bintrie_optimized.py. Since number of hash calls only depends on the number of proof nodes, there are much less hash calls in new_bintrie_optimized.py. I did some basic benchmarking on the forked code and for the same number of leaves, the simple SMT always has 256 proof nodes as expected whereas the optimized one has 10-15. You can find the benchmark code here.
