The Invertible Bloom Lookup Table (IBLT) is a probabilistic data structure for set representation, with applications in network and traffic monitoring. It is known for its ability to list its elements, an operation that succeeds with high probability for sufficiently large table. However, listing can fail even for relatively small sets. This paper extends recent work on the worst-case analysis of IBLT, which guarantees successful listing for all sets of a certain size, by introducing more general IBLT schemes. These schemes allow for greater freedom in the implementation of the insert, delete, and listing operations and demonstrate that the IBLT memory can be reduced while still maintaining successful listing guarantees. The paper also explores the time-memory trade-off of these schemes, some of which are based on linear codes and \(B_h\)-sequences over finite fields.

翻译：可逆布鲁姆查找表（IBLT）是一种用于集合表示的 probabilistic 数据结构，在网络与流量监控中具有应用。其以能够列出自身元素而闻名，对于足够大的表，该操作能以高概率成功。然而，即使对于规模较小的集合，列表操作也可能失败。本文扩展了近期关于IBLT最坏情况分析的工作，通过引入更通用的IBLT方案，保证对所有特定大小集合的成功列表。这些方案允许在插入、删除和列表操作的实现中拥有更大自由度，并表明在保持成功列表保证的同时，可以降低IBLT内存占用。本文还探讨了这些方案的时间-内存权衡，其中部分方案基于线性码及有限域上的\(B_h\)-序列。