Bloom filter based optimization scheme for massive data handling in IoT environment

Singh, Amritpal; Garg, Sahil; Batra, Shalini; Kumar, Neeraj; Rodrigues, Joel J. P. C.

doi:10.1016/j.future.2017.12.016

Cited by 55 publications

(37 citation statements)

References 33 publications

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“…Fourth is the hierarchical Bloom filter. Several methods are using this idea such as hierarchical segmented Bloom filter [7], Bloom filter-based framework [19], Accommodative Bloom filter (ABF) [20], etc. This type of Bloom filter utilizes several Bloom filters at multiple levels to increase scalability.…”

Section: Related Workmentioning

confidence: 99%

Scalable Two-Dimensional Bloom Filter Membership Scheme on Massive Scale Wireless Sensor Networks

Afianti¹,

Asrini²,

Wijaya³

2021

IJIES

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Wireless sensor nodes help people to monitor any operational mechanism. It is important to be implemented in industrial automation in detecting unauthorized activity or malicious sensor nodes by the outsider. However, a massive number of devices or things which are needed to be controlled become a challenge. A fast mechanism to recognize a member or a valid device is needed to minimize production costs and avoid future procedural errors. It becomes an open problem for the implementation of massive scale Wireless Sensor Networks (WSN). Hence, a scalable two-dimensional Bloom filter is proposed in this paper to deal with this issue. The low computation complexity and the storage efficiency requirements by the two-dimensional Bloom filter are proven by the experimental results. The insertion, deletion and query procedure of the proposed method only need O (1), while the storage usage of two-dimensional Bloom filters not only lower than counting Bloom filter in average 131 bits difference but also approaches accommodative Bloom filter in about 10 bits difference. These two parameters support a fast membership scheme. Furthermore, the analysis of value initialization has been performed. To the best of our knowledge, the investigation of this parameter has not been addressed by other studies. This process aims to get the best scenario to increase scalability. Several recommendations for selecting dimension number has been stated which is useful for efficient storage usage and reduce false positive.

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Section: Related Workmentioning

confidence: 99%

Scalable Two-Dimensional Bloom Filter Membership Scheme on Massive Scale Wireless Sensor Networks

Afianti¹,

Asrini²,

Wijaya³

2021

IJIES

View full text Add to dashboard Cite

show abstract

“…Big data management with widespread applications in IoT environment is unavoidable, therefore today, efficient storage media, high speed processing algorithms, and accessing of bulky data sets in short times are necessary. Recently, a variant of scalable bloom filter named as Accommodative bloom filter (ABF) is introduced by Singh et al [39] to solve these requirements.…”

Section: Related Workmentioning

confidence: 99%

Distributed SCFMBF Based Protocol for Integrity In Cloud Storage System (DPICS)

2020

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Cloud storage allowed the data owners to store their data without the burden of local hardware. There were some security concerns because of the cloud being untrusted. To make sure the stored data was intact since it was impractical to download the full data, a security mechanism named integrity check was used. Integrity check was achieved by probabilistic structures such as bloom filter and cuckoo filter. Uploading data file on to one cloud server could be a point of failure, instead the file could be distributed over l servers. Distributed SCFMBF Based Protocol for Integrity in Cloud Storage System (DPICS) was proposed to remotely check data integrity in a more effective way. Smart Cuckoo Filter Modified by Bloom Filter (SCFMBF) was a protocol that improved some of the performance properties of cuckoo filter such as false positive rate and insertion performance and solved some drawbacks of the cuckoo algorithm such as endless loop. Main characteristic of the bloom filter was used to improve cuckoo filter as the protocol name describes. First bloom filter was used as the integrity check method in cloud but bloom filter was not suitable for dynamic environment, SCFMBF was substituted for bloom filter because it had low false positive probability in integrity check and could cope well with dynamic data. How DPICS was more efficient than other previous works, was shown in terms of false positive probability, integrity check and retrievability.

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“…In other words, the increased number of queries sent to the LBS server (for privacy protection purposes) will result in both low performance (i.e., long response time) and pressure on the network (i.e., network overhead). [39]. This technique depends on a hash [k= key, V= value] that can give a direct answer about the existence or non-existence of an element within a given range.…”

Section: The Roles Of Componentsmentioning

confidence: 99%

Ensuring Privacy Protection in Location-based Services through Integration of Cache and Dummies

Innab¹,

Alaradi²

2019

ijacsa

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Location-Based Services (LBS) have recently gained much attention from the research community due to the openness of wireless networks and the daily development of mobile devices. However, using LBS is not risk free. Location privacy protection is a major issue that concerns users. Since users utilize their real location to get the benefits of the LBS, this gives an attacker the chance to track their real location and collect sensitive and personal information about the user. If the attacker is the LBS server itself, privacy issues may reach dangerous levels because all information related to the user's activities are stored and accessible on the LBS server. In this paper, we propose a novel location privacy protection method called the Safe Cycle-Based Approach (SCBA). Specifically, the SCBA ensures location privacy by generating strong dummy locations that are far away from each other and belong to different sub-areas at the same time. This ensures robustness against advanced inference attacks such as location homogeneity attacks and semantic location attacks. To achieve location privacy protection, as well as high performance, we integrate the SCBA approach with a cache. The key performance enhancement is storing the responses of historical queries to answer future ones using a bloom filter-based search technique. Compared to well-known approaches, namely the ReDS, RaDS, and HMC approaches, experimental results showed that the proposed SCBA approach produces better outputs in terms of privacy protection level, robustness against inference attacks, communication cost, cache hit ratio, and response time.

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Bloom filter based optimization scheme for massive data handling in IoT environment

Cited by 55 publications

References 33 publications

Scalable Two-Dimensional Bloom Filter Membership Scheme on Massive Scale Wireless Sensor Networks

Scalable Two-Dimensional Bloom Filter Membership Scheme on Massive Scale Wireless Sensor Networks

Distributed SCFMBF Based Protocol for Integrity In Cloud Storage System (DPICS)

Ensuring Privacy Protection in Location-based Services through Integration of Cache and Dummies

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