In-packet Bloom filters: Design and networking applications

RobustBF: A High Accuracy and Memory Efficient 2D Bloom Filter

Bloom Filter is an important probabilistic data structure to reduce memory consumption for membership filters. It is applied in diverse domains such as Computer Networking, Network Security and Privacy, IoT, Edge Computing, Cloud Computing, Big Data, and Biometrics. But Bloom Filter has an issue of the false positive probability. To address this issue, we propose a novel robust Bloom Filter, robustBF for short. robustBF is a 2D Bloom Filter, capable of filtering millions of data with high accuracy without compromising the performance. Our proposed system is presented in two-fold. Firstly, we m…

Pay for a Sliding Bloom Filter and Get Counting, Distinct Elements, and Entropy for Free

For many networking applications, recent data is more significant than older data, motivating the need for sliding window solutions. Various capabilities, such as DDoS detection and load balancing, require insights about multiple metrics including Bloom filters, per-flow counting, count distinct and entropy estimation. In this work, we present a unified construction that solves all the above problems in the sliding window model. Our single solution offers a better space to accuracy tradeoff than the state-of-the-art for each of these individual problems! We show this both analytically and by r…

Yes-no Bloom filter: A way of representing sets with fewer false positives

The Bloom filter (BF) is a space efficient randomized data structure particularly suitable to represent a set supporting approximate membership queries. BFs have been extensively used in many applications especially in networking due to their simplicity and flexibility. The performances of BFs mainly depends on query overhead, space requirements and false positives. The aim of this paper is to focus on false positives. Inspired by the recent application of the BF in a novel multicast forwarding fabric for information centric networks, this paper proposes the yes-no BF, a new way of representin…

In oder Aus

Bloom filters are data structures used to determine set membership of elements, with applications from string matching to networking and security problems. These structures are favored because of their reduced memory consumption and fast wallclock and asymptotic time bounds. Generally, Bloom filters maintain constant membership query time, making them very fast in their niche. However, they are limited in their lack of a removal operation, as well as by their probabilistic nature. In this paper, we discuss various iterations of and alternatives to the generic Bloom filter that have been resear…

Bloofi: Multidimensional Bloom Filters

Bloom filters are probabilistic data structures commonly used for approximate membership problems in many areas of Computer Science (networking, distributed systems, databases, etc.). With the increase in data size and distribution of data, problems arise where a large number of Bloom filters are available, and all them need to be searched for potential matches. As an example, in a federated cloud environment, each cloud provider could encode the information using Bloom filters and share the Bloom filters with a central coordinator. The problem of interest is not only whether a given element i…

技术报告：回收型布隆过滤器平均假阳性率建模

Technical Report: Modeling Average False Positive Rates of Recycling Bloom Filters

本文针对网络应用中回收型布隆过滤器，提出基于更新过程和马尔可夫模型的平均假阳性率计算方法，取代过于保守的最坏情况界限，并验证了模型准确性及两阶段变体的效率优势。