Probabilistic data structures for big data analytics: A comprehensive review

Singh, Amritpal; Garg, Sahil; Kaur, Ravneet; Batra, Shalini; Kumar, Neeraj; Zomaya, Albert Y.

doi:10.1016/j.knosys.2019.104987

Cited by 43 publications

(21 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is due to the large memory and high latency issues for processing queries using these traditional data structures. The probabilistic data structure, as a kind of data structure, is particularly advantageous for large data, because it reduces latency, and analytical procedures [27]. They are tremendously handy data systems for reducing the space and time trade-off, and to a great extent, equivalent to retrieval and storage for querying of data [28].…”

Section: Probabilistic Data Structurementioning

confidence: 99%

A security and privacy scheme based on node and message authentication and trust in fog-enabled VANET

Soleymani

Goudarzi

Anisi

et al. 2021

Vehicular Communications

View full text Add to dashboard Cite

Section: Probabilistic Data Structurementioning

confidence: 99%

A security and privacy scheme based on node and message authentication and trust in fog-enabled VANET

Soleymani

Goudarzi

Anisi

et al. 2021

Vehicular Communications

View full text Add to dashboard Cite

“…Hence, it is assumed that the probabilistic data structure, as a group of data structures, is used in the proposed model. This kind of data structure is extremely useful for big data, because it reduces latency and the analytical process [18]. The bloom filter (BF), cuckoo filter (CF) and quotient filter (QF) are three different types of space-efficient probabilistic data structures that are used to check whether an element is a member of a massive dataset or not.…”

Section: Type Of Node Detectionmentioning

confidence: 99%

A Trust Model Using Edge Nodes and a Cuckoo Filter for Securing VANET under the NLoS Condition

et al. 2020

View full text Add to dashboard Cite

Trust, as a key element of security, has a vital role in securing vehicular ad-hoc networks (VANETs). Malicious and selfish nodes by generating inaccurate information, have undesirable impacts on the trustworthiness of the VANET environment. Obstacles also have a negative impact on data trustworthiness by restricting direct communication between nodes. In this study, a trust model based on plausibility, experience, and type of vehicle is presented to cope with inaccurate, incomplete and uncertainty data under both line of sight (LoS) and none-line of sight (NLoS) conditions. In addition, a model using the k-nearest neighbor (kNN) classification algorithm based on feature similarity and symmetry is developed to detect the NLoS condition. Radio signal strength indicator (RSSI), packet reception rate (PDR) and the distance between two vehicle nodes are the features used in the proposed kNN algorithm. Moreover, due to the big data generated in VANET, secure communication between vehicle and edge node is designed using the Cuckoo filter. All obtained results are validated through well-known evaluation measures such as precision, recall, overall accuracy, and communication overhead. The results indicate that the proposed trust model has a better performance as compared to the attack-resistant trust management (ART) scheme and weighted voting (WV) approach. Additionally, the proposed trust model outperforms both ART and WV approaches under different patterns of attack such as a simple attack, opinion tampering attack, and cunning attack. Monte-Carlo simulation results also prove validity of the proposed trust model.

show abstract

“…A rather new technique to approach the problem of processing large datasets are Probabilistic Data Structures [41]. A specific example is a cardinality estimation algorithm called HyperLogLog [42].…”

Section: Privacy-aware Storagementioning

confidence: 99%

Case Study on Privacy-Aware Social Media Data Processing in Disaster Management

Löchner

Fathi

Schmid

et al. 2020

IJGI

View full text Add to dashboard Cite

Social media data is heavily used to analyze and evaluate situations in times of disasters, and derive decisions for action from it. In these critical situations, it is not surprising that privacy is often considered a secondary problem. In order to prevent subsequent abuse, theft or public exposure of collected datasets, however, protecting the privacy of social media users is crucial. Avoiding unnecessary data retention is an important question that is currently largely unsolved. There are a number of technical approaches available, but their deployment in disaster management is either impractical or requires special adaption, limiting its utility. In this case study, we explore the deployment of a cardinality estimation algorithm called HyperLogLog into disaster management processes. It is particularly suited for this field, because it allows to stream data in a format that cannot be used for purposes other than the originally intended. We develop and conduct a focus group discussion with teams of social media analysts. We identify challenges and opportunities of working with such a privacy-enhanced social media data format and compare the process with conventional techniques. Our findings show that, with the exception of training scenarios, deploying HyperLogLog in the data acquisition process will not distract the data analysis process. Instead, several benefits, such as improved working with huge datasets, may contribute to a more widespread use and adoption of the presented technique, which provides a basis for a better integration of privacy considerations in disaster management.

show abstract

Probabilistic data structures for big data analytics: A comprehensive review

Cited by 43 publications

References 42 publications

A security and privacy scheme based on node and message authentication and trust in fog-enabled VANET

A security and privacy scheme based on node and message authentication and trust in fog-enabled VANET

A Trust Model Using Edge Nodes and a Cuckoo Filter for Securing VANET under the NLoS Condition

Case Study on Privacy-Aware Social Media Data Processing in Disaster Management

Contact Info

Product

Resources

About