Fog computing‐based privacy preserving data aggregation protocols

Okay, Feyza Yıldırım; Özdemir, Suat; Xiao, Yang

doi:10.1002/ett.3900

Cited by 10 publications

(8 citation statements)

References 68 publications

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“…Result of extensive performance evaluation and analysis shows that in comparison to cloudassisted schemes which involves no data aggregation the proposed protocols are far superior considering data transmission and storage efficiency. Besides, it was verified in privacy analysis that these two protocols ensure each layer's data privacy in an effective way [12].…”

Section: Related Workmentioning

confidence: 97%

“…Now this time is to use the Calculate the Dynamic key using calculation matrix and formula for this is like Step Dynamic key (x) = calculation matrix [key(x)] [Plain text(x)]For every character of plain text and key we will trace character for dynamic key. By taking the key in X-axis and Plain text in Y-Axis Taking this example forward as we have the Numeric Plain text as[22,1,12, 21,5, 19]…”

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confidence: 99%

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A Novel Framework for Privacy-Preserving Using Data Aggregation in Internet of Things (Iot)

Mohapatra¹

2020

ijarcs

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Today with the development of Internet of Things (IoT) technology its security and privacy becomes very much essential because this technology provide the facility for accessing the devices or resources from anywhere and anytime. For providing the security and privacy to IoT various technologies has been developed but in this we propose data aggregation mechanism in which it uses one-time pad and symmetric key to make our data secure and preserve the privacy of it. This mechanism takes less computation time, improve processing speed and also require less cost in implementation. The implementation of proposed approach is done in widely used Python technology which produces better results. In last the proposed mechanism provides more security and preserves our data.

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Section: Related Workmentioning

confidence: 97%

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confidence: 99%

A Novel Framework for Privacy-Preserving Using Data Aggregation in Internet of Things (Iot)

Mohapatra¹

2020

ijarcs

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“…Take the first pass as an example, as the first super-bin corresponds to the two least significant bits, pattern is initialized as (Line 5 in Algorithm 2): pattern ←0000 … 000011 (64-bit) By conducting bitwise AND operation over pattern and each column imprint (ie, imps[i]), we can determine whether corresponding cacheline contains values that fall into the first partition. If positive, each value v in that cacheline is accessed, and only probed when v is within the range of the first partition, that is, (− ∞,bins [1]). After examining all the imprints, pattern is modified and ready for the next pass.…”

Section: Multi-passmentioning

confidence: 99%

“…The volume of data collected from those edge devices are huge and thus big data processing becomes an emerging topic in telecommunication fields these years. And in such a big data scenario, many researchers has studied data aggregation, 1 data indexing, 2 data communication, 3 data collection, 4 and data sharing 5 in emerging telecommunication fields. Beside those data techniques, data storing has also attracted wide attentions, and it has witnessed the increasing popularity of modern main-memory column-stores like SAP HANA, 6 MonetDB, 7 Vectorwise, 8 and Oracle Exalytics.…”

Section: Introductionmentioning

confidence: 99%

Utilizing the column imprints to accelerate no‐partitioning hash joins in large‐scale edge systems

Liu

2020

Trans Emerging Tel Tech

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With the increasing number of edge devices in large-scale edge systems, more and more data are collected to be processed. In such big data scenarios, there is a resurgence of interest in main-memory analytic databases because of the large RAM capacity of modern servers and the increasing demand for real-time analytic platforms. In such databases, join is at the heart of almost every query plan. Join also stays as a time-consuming operation when the denormalization overhead is too large to be applicable. However, the current implementations of these operations have not fully leveraged the new features (eg, SIMD, multi-core) provided by the modern hardware. The goal of this article is to design efficient algorithms for joins by judiciously exploiting every bit of RAM and all the available parallelisms in each processing unit. For join operations, hash joins have been studied, improved, and reexamined over decades.In this article, we propose to utilize a secondary index to improve hash joins without the physical partitioning. Specifically, in the build phase, the hash values are scattered evenly into the logical partitions of the hash table; in the probe phase, the secondary index is used as the hints to re-order the probing sequence, such that the locality of the hash probing is increased. We benchmark the performance of the proposed techniques in our column-store research prototype. Extensive experiments on the synthetic data and the real data show that our methods offer significant performance improvement over their counterparts.

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“…After the introduction of FOG computing by Cisco [1], considering the capability of FOG to provide computing at the edge of the network while preserving the advantages of cloud computing such as ubiquity, decentralized management, and so on, FOG-assisted CPS (FOG-CPS) [2][3][4][5][6][7][8] is emerging as a new research domain to address the data management issues of CPS.…”

Section: Introductionmentioning

confidence: 99%

Certificateless Reliable and Privacy-Preserving Auditing of Group Shared Data for FOG-CPSs

Burra

Maity

2022

Security and Communication Networks

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FOG-enabled cyber-physical systems (FOG-CPSs) open new security challenges as the local edge devices are easier to compromise than a traditional cloud server. Remote data integrity checking (RDIC) plays an important role in safeguarding against data corruption from a storage server. Certificateless cryptography (CLPKC)-based RDIC schemes do not suffer from the drawbacks of the public key infrastructure (PKI)-based RDIC protocols. Most of the CLPKC-based RDIC schemes proposed in the literature deal with personal data. However, in a FOG-CPS, it is also important to audit a data file shared by a group of edge devices. Most of the existing group shared data auditing schemes lack mechanisms to defend against a semi-trusted data auditor applicable for a FOG-CPS scenario. In order to address these issues, in this paper, we propose a novel CLPKC-based group shared data auditing protocol tailored to the specific security requirements of a FOG-CPS. Besides, we perform a detailed cryptanalysis of two existing CLPKC-based privacy-preserving group shared data auditing schemes. The formal security analysis of our proposed protocol establishes metadata and data integrity proof unforgeability and claimed zero-knowledge privacy and reliability properties through rigorous proofs in the random oracle model setting. Performance evaluations establish the efficiency of our proposed protocol.

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Fog computing‐based privacy preserving data aggregation protocols

Cited by 10 publications

References 68 publications

A Novel Framework for Privacy-Preserving Using Data Aggregation in Internet of Things (Iot)

A Novel Framework for Privacy-Preserving Using Data Aggregation in Internet of Things (Iot)

Utilizing the column imprints to accelerate no‐partitioning hash joins in large‐scale edge systems

Certificateless Reliable and Privacy-Preserving Auditing of Group Shared Data for FOG-CPSs

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