Adaptive One Memory Access Bloom Filters

Reviriego, Pedro; Sánchez‐Macian, Alfonso; Rottenstreich, Ori; Larrabeiti, David

doi:10.1109/tnsm.2022.3145436

Cited by 9 publications

(5 citation statements)

References 29 publications

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“…For those, it may be beneficial upon detecting a false positive to adapt the filter so that subsequent queries to the same elements return a negative [38]. Adaptive filters based on Bloom [39], cuckoo [40] and QFs [38], [41] have been proposed.…”

Section: Adaptive Filtersmentioning

confidence: 99%

On the Security of Quotient Filters: Attacks and Potential Countermeasures

Reviriego,

González,

Dayan

et al. 2024

IEEE Trans. Comput.

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The security of probabilistic data structures is increasingly important due to their wide adoption in many computing systems and applications. In particular, the security of approximate membership check filters such as Bloom or cuckoo filters has been recently studied showing how an attacker can degrade the filter performance in some settings. In this paper, we consider for the first time the security of another popular approximate membership check filter, the Quotient Filter (QF). Our analysis and simulations show that quotient filters are vulnerable to both white and black box attackers that can cause insertion failures and degrade the filter performance very significantly. An interesting finding is that quotient filters are vulnerable to a new type of attack, not applicable to Bloom or cuckoo filters, that can degrade the speed of queries dramatically. The paper also briefly discusses and evaluates potential countermeasures to detect and protect against those attacks.

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Section: Adaptive Filtersmentioning

confidence: 99%

On the Security of Quotient Filters: Attacks and Potential Countermeasures

Reviriego,

González,

Dayan

et al. 2024

IEEE Trans. Comput.

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“…The algorithm, which was adjusted to the bucket size, can preserve the support for deletion of the original cuckoo filter without the additional memory accesses for lookup operations. To effectively remove false positives while completing all queries in a single memory access, the authors of [24] proposed an adaptation scheme based on one memory access bloom filter. The proposed algorithm is very suitable for the case of low memory bits per element.…”

Section: Quick Lookup Mechanismmentioning

confidence: 99%

Seismic Data Query Algorithm Based on Edge Computing

Quan

Zhang

et al. 2023

Electronics

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Edge computing can reduce the transmission pressure of wireless networks in earthquakes by pushing computing functionalities to network edges and avoiding the data transmission to cloud servers. However, this also leads to the scattered storage of data content in each edge server, increasing the difficulty of content search. This paper investigates the seismic data query problem supported by edge computing. We first design a lookup mechanism based on bloom filter, which can quickly determine if there is the information that we need on a particular edge server. Then, the MEC-based data query problem is formulated as an optimization problem whose goal is to minimize the long-term average task delay with the constraints of computing capacity of edge servers. To reduce the complexity of problem, we further transform it as a Markov Decision Process by defining state space, action space and reward function. A novel DQN-based seismic data query algorithm is proposed to solve problem effectively. Extensive simulation-based testing shows that the proposed algorithm performances better when compared with two state-of-the-art solutions.

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“…Reviriego et al [15] introduced an adaptive memory access bloom filter (BF) that specifically addresses the issue of false positives when dealing with repeated elements across different queries. Their approach aims to minimize false positive occurrences in such scenarios.…”

Section: Background and Related Workmentioning

confidence: 99%

Content-Based Approach for Improving Bloom Filter Efficiency

et al. 2023

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Bloom filters are a type of data structure that is used to test whether or not an element is a member of a set. They are known for being space-efficient and are commonly employed in various applications, such as network routers, web browsers, and databases. These filters work by allowing a fixed probability of incorrectly identifying an element as being a member of the set, known as the false positive rate (FPR). However, traditional bloom filters suffer from a high FPR and extensive memory usage, which can lead to incorrect query results and a slow performance. Thus, this study indicates that a content-based strategy could be a practical solution for these challenges. Specifically, our approach requires less bloom filter storage, consequently decreasing the probability of false positives. The effectiveness of several hash functions on our strategy’s performance was also evaluated. Experimental evaluations demonstrated that the proposed strategy could potentially decrease false positives by a substantial margin of up to 79.83%. The use of size-based content bits significantly contributes to the decrease in the number of false positives as well. However, as the volume of content bits rises, the impact on time is not considerably noticeable. Moreover, the evidence suggests that the application of a singular approach leads to a more than 50% decrease in false positives.

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Adaptive One Memory Access Bloom Filters

Cited by 9 publications

References 29 publications

On the Security of Quotient Filters: Attacks and Potential Countermeasures

On the Security of Quotient Filters: Attacks and Potential Countermeasures

Seismic Data Query Algorithm Based on Edge Computing

Content-Based Approach for Improving Bloom Filter Efficiency

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