In the solution explained below, we enable a peer to to perform the operations described in [Formal Problem Definition](#Formal-Problem-Definition) by only holding O(logn) many tree nodes (but not the entire tree).
> In a Merkle Tree with the capacity of `n` leaves , one can compute the root of the tree by maintaining the root nodes of log(n) number of complete Merkle trees.
We use the preciding observation and define `F = [(L:0, H0, leafIndex0), ..., (L:d, Hd, leafIndexd)]` to be an array of size log(n)+1 holding the root of the complete Merkle trees (all positioned on the left side of the tree) for levels `[0, ..., d=log(n)]`. Each entry of `F` is a tuple `(L, leafIndex, H)` in which `H`is the root of the complete subtree at level `L`, and `leafIndex` indicates the index of the leaf node whose insertion resulted in `H`. The storage of `leafIndex` in each tuple will later enables the efficient support of deletion operation. Each peer shall store `F` locally.
For the Merkle Tree shown in Figure below, `F = [(L:0, N12, leafIndex:5), (L:1, N6, leafIndex:6), (L:2, N2, leafIndex:4), (L:3, N1, leafIndex:6)]` is highlighted in green. Note that `F` only contains the green nodes but none of the gray nodes.
Given that `F` is stored by each peer locally, we demonestrate how deletion can be supported relying on `F`.
When a node gets deleted, its authentication path is available, and the following solution relies on that.
Consider the deletion of the `leafIndex:i` with the authentication path / (membership proof) of the follwoing form `authpath = [(L:0, H0), ..., (L:d, Hd)]` where in each tuple `H` represents the value of the merkle tree node along the authentication path of leaf `i` at the corresponding level `L`. Note that `d` varies from `0` to `logn`. The last entry of `authpath` i.e., `(L:d, Hd)` is indeed the tree root.
The authentication path of `leafIndex:2` is illustrated in the following figure (highlighted in yellow) and consists of `authpath2 = [(L:0, N8), (L:1, N5), (L:2, N3), (L:3, N1)]`.
We need to update `F` based on `authpath2`. In specific, we need to determine whether any of the nodes whose values get altered as the result of deletion of a leaf node intersect with the nodes in `F`, and if this is the case the corresponding nodes in `F` shall get updated too.
Lets clarify it by the help of an example. Consider `leafIndex:2`, the deletion of `leafIndex:2` impacts `N9 (level 0), N4 (level 1), N2 (level 2)` and `N1 (level 3)`(root) of the tree, as illustrated below with the dark circles.
Thus, in order to update `F`, we need to update those entries of `F` that contain `N9, N4, N2` or `N1`.
To do so, we determine whether for each tuple `(L, leafIndex, H)` in `F`, the `leafIndex` and the deleted leaf node have the same ancestor at level `L`. We use `HasCommAnc` method to perform this check, it is later defined in [Common ancestor](###Common-Ancestor ) subsection. For example, `leaf2` and `leaf4` have the same ancestor at level 2, which is `N2`, thus `HasCommAnc(2,4,2)` return true.
Following our previous example, in order to find out whether `N9, N4, N2` or `N1` belong to `F` we proceed as follows.
In order to determine whether two nodes with indices `i` and `j` have common anscestor at a particular level `lev`, the followong formula can be applied
check whether `floor( (i-1)/2^lev )` is equal to `floor( (j-1)/2^lev )`
Lets index the tree nodes following the formula below, for each tree node with index `i`, its right and left child have index `2*i` and `2*i+1`, respectively. The root has index `1`. The sample tree is illustrated below.
When a node gets deleted,
While writing up the preceding solution, I came up with a simiplified version in which we do not need to store the the leaf indices, instead we can compute them on the fly.
