Restructuring a Patricia-Merkle Tree Fragment Through Leaf Pair Swapping

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11 Sept 2024

Authors:

(1) Oleksandr Kuznetsov, Proxima Labs, 1501 Larkin Street, suite 300, San Francisco, USA and Department of Political Sciences, Communication and International Relations, University of Macerata, Via Crescimbeni, 30/32, 62100 Macerata, Italy (kuznetsov@karazin.ua);

(2) Dzianis Kanonik, Proxima Labs, 1501 Larkin Street, suite 300, San Francisco, USA;

(3) Alex Rusnak, Proxima Labs, 1501 Larkin Street, suite 300, San Francisco, USA (alex@proxima.one);

(4) Anton Yezhov, Proxima Labs, 1501 Larkin Street, suite 300, San Francisco, USA;

(5) Oleksandr Domin, Proxima Labs, 1501 Larkin Street, suite 300, San Francisco, USA.

Abstract and 1. Introduction

1.1. The Blockchain Paradigm and the Challenge of Scalability

1.2. State of the art

1.3. Our contribution and 1.4. Article structure

2. Conceptualizing the Problem

3. Our Idea for Optimizing Trees in Blockchain

4. Efficiency of adaptive Merkle trees

5. Algorithm for Merkle Tree Restructuring

6. Examples of Merkle Tree Restructuring Algorithm Execution and 6.1 Example 1: Restructuring a Binary Tree by Adding One Leaf

6.2. Example 1.1: Binary Tree Restructuring Through Leaf Node Swapping

6.3. Example 2.1: Restructuring a Non-Binary Tree by Adding a Single Leaf

6.4. Example 2.2: Restructuring a Non-Binary Tree Through Leaf Pair Swapping

6.5. Example 2.3: Restructuring a Patricia-Merkle Tree Fragment Through Leaf Pair Swapping

7. Path Encoding in the Adaptive Merkle Tree

8. Enhancing Verkle Trees Through Adaptive Restructuring and 8.1. Application of Adaptive Trees in Verkle Tree Technology

8.2. Technology and Advantages

9. Discussion

9.1. Our Contribution

9.2. Comparison with Existing Solutions

10. Conclusion and References

6.5. Example 2.3: Restructuring a Patricia-Merkle Tree Fragment Through Leaf Pair Swapping

Imagine we have a fragment of the Patricia-Merkle tree with leaves (and probabilities) assigned as follows (see Figure 31):

A (0.003906), B (0.0625), C (0.16529), D (0.0625), E (0.0625), F (0.0625), G (0.0625), H (0.0625), I (0.003906), J (0.0625), K (0.0625), L (0.0625), M (0.0625), N (0.003906), O (0.0625), P (0.000244), Q (0.0625), R (0.01), S (0.0625), T (0.000244).

Figure 31: Fragment of a Patricia-Merkle Tree with m =16

Figure 32: Result of the First Optimization of the Patricia-Merkle Tree with m =16

Figure 33: Result of the Second Optimization of the Patricia-Merkle Tree with m =16

Thus, even a small number of algorithm iterations allows for a significant reduction in discrepancy (1) and optimization of the tree structure, reducing the average path length.

This example underscores the potential of our restructuring algorithm to enhance the efficiency of Patricia-Merkle trees in blockchain applications. By judiciously swapping the positions of leaf pairs, we can significantly improve the tree's structure, aligning it closer to the optimal configuration. This process not only minimizes the average path length but also contributes to the overall efficiency and scalability of blockchain operations, particularly in systems like Ethereum where Patricia-Merkle trees play a crucial role in data integrity verification.

This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license.