Autoscaling Bloom filter: controlling trade-off between true and false positives

Kleyko, Denis; Rahimi, Abbas; Gayler, Ross W.; Osipov, Evgeny

doi:10.1007/s00521-019-04397-1

Cited by 35 publications

(18 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This may be beneficial when the alphabet size is large enough that storing codewords is not possible. The connections between HD computing and Bloom filters are examined in greater detail in (Kleyko et al, 2019).…”

Section: Sparse and Low-precision Encodingsmentioning

confidence: 99%

A Theoretical Perspective on Hyperdimensional Computing

ThomasAnthony¹,

DasguptaSanjoy²,

RosingTajana³

2021

jair

View full text Add to dashboard Cite

Hyperdimensional (HD) computing is a set of neurally inspired methods for obtaining highdimensional, low-precision, distributed representations of data. These representations can be combined with simple, neurally plausible algorithms to effect a variety of information processing tasks. HD computing has recently garnered significant interest from the computer hardware community as an energy-efficient, low-latency, and noise-robust tool for solving learning problems. In this review, we present a unified treatment of the theoretical foundations of HD computing with a focus on the suitability of representations for learning.

show abstract

Section: Sparse and Low-precision Encodingsmentioning

confidence: 99%

A Theoretical Perspective on Hyperdimensional Computing

ThomasAnthony¹,

DasguptaSanjoy²,

RosingTajana³

2021

jair

View full text Add to dashboard Cite

show abstract

“…When all k values in the hash functions are 1, but the item q is not in S, false positives occur. B denotes bloom filter; H denotes hash function [58].…”

Section: Preliminariesmentioning

confidence: 99%

BL0K: A New Stage of Privacy-Preserving Scope for Location-Based Services

Albelaihy

Thayananthan

2019

Sensors

View full text Add to dashboard Cite

Location-based services present an inherent challenge of finding the delicate balance between efficiency when answering queries and maintaining user privacy. Inevitable security issues arise as the server needs to be informed of the query location to provide accurate responses. Despite the many advancements in localization security in wireless sensor networks, servers can still be infected with malicious software. It is now possible to ensure queries do not generate any fake responses that may appear real to users. When a fake response is used, there are mechanisms that can be employed so that the user can identify the authenticity of the query. For this reason, this paper proposes Bloom Filter 0 Knowledge (BL0K), which is novel phase privacy method that preserves the framework for location-based service (LBS) and combines a Bloom filter and the Zero knowledge protocol. The usefulness of these methods has been shown for securing private user information. Analysis of the results demonstrated that BL0K performance is decidedly better when compared to the referenced approaches using the privacy entropy metric.

show abstract

“…In [57], a Bloom filter version is analyzed which recognizes the absence of distorted query vectors. The autoscaling Bloom filter approach proposed in [92] suggests a generalization of the counting Bloom filter approach based on the mathematics of sparse hyperdimensional computing and allows elastic adjustment of its capacity with probabilistic bounds on false positives and true positives. In [90], the formation of sparse memory vectors (with an additional operation of context-dependent thinning [134]) is considered, and in [91] the probability of correct recognition is estimated.…”

Section: The Generalization Of Krotov-hopfieldmentioning

confidence: 99%

Neural Autoassociative Memories for Binary Vectors: A Survey

Gritsenko¹,

Rachkovskij²,

Frolov³

et al. 2017

Kibern. vyčisl. teh.

View full text Add to dashboard Cite

Introduction. Neural network models of autoassociative, distributed memory allow storage and retrieval of many items (vectors) where the number of stored items can exceed the vector dimension (the number of neurons in the network). This opens the possibility of a sublinear time search (in the number of stored items) for approximate nearest neighbors among vectors of high dimension.The purpose of this paper is to review models of autoassociative, distributed memory that can be naturally implemented by neural networks (mainly with local learning rules and iterative dynamics based on information locally available to neurons).Scope. The survey is focused mainly on the networks of Hopfield, Willshaw and Potts, that have connections between pairs of neurons and operate on sparse binary vectors. We discuss not only autoassociative memory, but also the generalization properties of these networks. We also consider neural networks with higher-order connections and networks with a bipartite graph structure for non-binary data with linear constraints.Conclusions. In conclusion we discuss the relations to similarity search, advantages and drawbacks of these techniques, and topics for further research. An interesting and still not completely resolved question is whether neural autoassociative memories can search for approximate nearest neighbors faster than other index structures for similarity search, in particular for the case of very high dimensional vectors. Introduction. Neural network models of autoassociative, distributed memory allow storage and retrieval of many items (vectors) where the number of stored items can exceed the vector dimension (the number of neurons in the network). This opens the possibility of a sublinear time search (in the number of stored items) for approximate nearest neighbors among vectors of high dimension.The purpose of this paper is to review models of autoassociative, distributed memory that can be naturally implemented by neural networks (mainly with local learning rules and iterative dynamics based on information locally available to neurons).Scope. The survey is focused mainly on the networks of Hopfield, Willshaw and Potts, that have connections between pairs of neurons and operate on sparse binary vectors. We (Online), ISSN 0454-9910 (Print). Киб. и выч. техн. 2017. № 2 (188) 6 discuss not only autoassociative memory, but also the generalization properties of these networks. We also consider neural networks with higher-order connections and networks with a bipartite graph structure for non-binary data with linear constraints.Conclusions. In conclusion we discuss the relations to similarity search, advantages and drawbacks of these techniques, and topics for further research. An interesting and still not completely resolved question is whether neural autoassociative memories can search for approximate nearest neighbors faster than other index structures for similarity search, in particular for the case of very high dimensional vectors. ISSN 2519-2205 (Online), ISSN 0454-9910 (Print). Киб...

show abstract

Autoscaling Bloom filter: controlling trade-off between true and false positives

Cited by 35 publications

References 36 publications

A Theoretical Perspective on Hyperdimensional Computing

A Theoretical Perspective on Hyperdimensional Computing

BL0K: A New Stage of Privacy-Preserving Scope for Location-Based Services

Neural Autoassociative Memories for Binary Vectors: A Survey

Contact Info

Product

Resources

About