改进的低开销通信高效字符串协调与编辑距离算法
Improved Low-Overhead Communication-Efficient String Reconciliation and Edit Distance
中文总结 AI 辅助
本文针对持有长字符串的两方需确定字符串相似性且通信成本与差异度成正比的问题，提出低开销通信高效算法，仅用O(k log³ n)比特通信量与O(n log k)时间开销即可高概率确定两字符串的编辑距离k。
中文摘要 AI 辅助
假设两方，Alice和Bob，分别持有长字符串X和Y，他们希望确定X和Y的相似程度，且希望交换字符串的成本与二者的差异程度成正比。这类问题出现在数据库与文件系统同步操作、DNA序列比较等场景中。由于字符串较长，当二者足够相似时，我们关注通信高效且Alice、Bob计算开销低的方法。本文针对字符串协调与编辑距离问题，提出了一种简单的低开销通信高效算法，可高概率仅使用O(k log³ n)比特的通信量与O(n log k)的时间开销，确定X和Y之间的编辑距离k。
英文摘要
Suppose two parties, Alice and Bob, hold long character strings, $X$ and $Y$, respectively, and they are interested in determining how similar $X$ and $Y$ are. {Moreover, they want to exchange the strings with cost proportional to their degree of dissimilarity.} Such problems arise, for example, in database and file system synchronization operations, as well as in DNA sequence comparisons. Since the strings are long, we are interested in methods that are communication-efficient and have low overhead in terms of the computations that Alice and Bob must perform, when the strings are similar enough. In this paper, we provide a simple low-overhead communication-efficient algorithms for such string reconciliation and edit distance problems, determining the edit distance $k$ between $X$ and $Y$ using only $O(k\log^3 n)$ bits of communication and $O(n\log k)$ time overhead, with high probability.
