# Autoscaling Bloom Filter: Controlling Trade-off Between True and False Positives

arXiv: https://arxiv.org/abs/1705.03934 · HTML: https://mlsanity.com/paper/1705.03934/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2017.

## Built on by (most cited first, 12 of 12)

- [A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations](https://mlsanity.com/paper/2111.06077/) (2021)
- [A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges](https://mlsanity.com/paper/2112.15424/) (2021)
- [Computing on Functions Using Randomized Vector Representations](https://mlsanity.com/paper/2109.03429/) (2021)
- [Vector Symbolic Architectures as a Computing Framework for Emerging Hardware](https://mlsanity.com/paper/2106.05268/) (2021)
- [Generalized Learning Vector Quantization for Classification in Randomized Neural Networks and Hyperdimensional Computing](https://mlsanity.com/paper/2106.09821/) (2021)
- [Hyperseed: Unsupervised Learning with Vector Symbolic Architectures](https://mlsanity.com/paper/2110.08343/) (2021)
- [Hyperdimensional Computing for Efficient Distributed Classification with Randomized Neural Networks](https://mlsanity.com/paper/2106.00881/) (2021)
- [Generalized Holographic Reduced Representations](https://mlsanity.com/paper/2405.09689/) (2024)
- [Streaming Encoding Algorithms for Scalable Hyperdimensional Computing](https://mlsanity.com/paper/2209.09868/) (2022)
- [On Effects of Compression with Hyperdimensional Computing in Distributed Randomized Neural Networks](https://mlsanity.com/paper/2106.09831/) (2021)
- [Hyperdimensional computing: a fast, robust and interpretable paradigm for biological data](https://mlsanity.com/paper/2402.17572/) (2024)
- [HyperCam: Low-Power Onboard Computer Vision for IoT Cameras](https://mlsanity.com/paper/2501.10547/) (2025)

## Abstract (arXiv)

A Bloom filter is a simple data structure supporting membership queries on a set. The standard Bloom filter does not support the delete operation, therefore, many applications use a counting Bloom filter to enable deletion. This paper proposes a generalization of the counting Bloom filter approach, called "autoscaling Bloom filters", which allows adjustment of its capacity with probabilistic bounds on false positives and true positives. In essence, the autoscaling Bloom filter is a binarized counting Bloom filter with an adjustable binarization threshold. We present the mathematical analysis of the performance as well as give a procedure for minimization of the false positive rate.