A novel data auditing approach to achieve data privacy and data integrity in cloud computing

Hiremath, Shivarajkumar; Kunte, Sanjeev R

doi:10.1109/iceeccot.2017.8284517

Cited by 20 publications

(14 citation statements)

References 7 publications

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“…When health data are collected and processed across heterogeneous networks, it requires compliance using a template, as well as defining data requested for certain applications. To achieve this, it is required to be able to send an automatic data request for immediate inquiries, including data integrity to inference in cloud storage to verify the integrity of a third party for security purposes [11][12][13], when an additional request should be sent out to all networks. We will implement and experiment to measure the network response time with accuracy in this case, as well as how accurately the proposed method can provide the best optimized data sampling at large scale using big data in the cloud server, which may be located in a centralized location, in the context of software defined network (SDN) infrastructure with fault tolerant mechanisms [14,15].…”

Section: Discussionmentioning

confidence: 99%

An Enhanced Inference Algorithm for Data Sampling Efficiency and Accuracy Using Periodic Beacons and Optimization

Kang

Fahd²,

Venkatraman³

2019

BDCC

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Transferring data from a sensor or monitoring device in electronic health, vehicular informatics, or Internet of Things (IoT) networks has had the enduring challenge of improving data accuracy with relative efficiency. Previous works have proposed the use of an inference system at the sensor device to minimize the data transfer frequency as well as the size of data to save network usage and battery resources. This has been implemented using various algorithms in sampling and inference, with a tradeoff between accuracy and efficiency. This paper proposes to enhance the accuracy without compromising efficiency by introducing new algorithms in sampling through a hybrid inference method. The experimental results show that accuracy can be significantly improved, whilst the efficiency is not diminished. These algorithms will contribute to saving operation and maintenance costs in data sampling, where resources of computational and battery are constrained and limited, such as in wireless personal area networks emerged with IoT networks.

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Section: Discussionmentioning

confidence: 99%

An Enhanced Inference Algorithm for Data Sampling Efficiency and Accuracy Using Periodic Beacons and Optimization

Kang

Fahd²,

Venkatraman³

2019

BDCC

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“…Where,  denotes the activation function. Therefore, the output attained from the first RBM is expressed as equation (18).…”

Section: Commented [Hp12]mentioning

confidence: 99%

“…CC is a paradigm where the resources of computing are shared as a cloud service on the internet [18]. CC is used in many organizations in the worldwide level as it supports computation and data storage with high performance.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-Taylor-elephant herd optimization inspired Deep Belief Network for DDoS attack detection and comparison with state-of-the-arts algorithms

Velliangiri¹,

Pandey

2020

Future Generation Computer Systems

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Cloud computing environment support resource sharing as cloud service over the internet. It enables the users to outsource data into the cloud server that can be accessed remotely from various devices distributed geographically. Accessing resources from the cloud causes various security issues as the attackers try to illegally access the data. The distributed denial of service (DDoS) attack is one of the security concern in the cloud server. DDoS is a kind of cyber attack which disrupt normal traffic of targeted cloud server (or any other servers). In this paper, we propose an effective fuzzy and taylor-elephant herd optimization (FT-EHO) inspired by deep belief network (DBN) classifier for detecting the DDoS attack. FT-EHO uses taylor series and elephant heard optimization algorithm along with a fuzzy classifier for rules learning. The performance of the proposed FT-EHO is evaluated through rigorous computer simulations. Three standard benckmark databases, namely, KDD cup, database1 and database2 are used during simulations. Four quality measures such as accuracy, detection accurarcy, precision and recall are considered as a performance metrics. FT-EHO's performance is compared against the state-ofthe-art methods considering the evaluation metrics. Results reveals that the proposed FT-EHO showed significantly higher value of evaluation metrics (accuracy (93.811%), detection rate (97.200%), precision (94.981%) and recall (93.833%)) as compared to other methods.

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“…Through signal theory, they identify which are the keys that most influence its effectiveness. Agarkhed and Ashalatha 25 and Hiremath and Kunte 26 described a dedicated framework for data storage auditing in the cloud. Agarkhed and Ashalatha 25 provided auditing to check the correctness of outsourced data avoiding that information could be accessed by unauthorized users or hackers in the unsecured cloud network.…”

Section: Related Workmentioning

confidence: 99%

A trust assurance technique for Internet of things based on human behavior compliance

Anisetti

Ardagna

Damiani

et al. 2019

Concurrency and Computation

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The advent of the Internet of Things (IoT) has radically changed the way in which computations and communications are carried out. People are just becoming another component of IoT environments and, in turn, IoT environments are becoming a mixture of platforms, software, services, things, and people. The price we pay for such dynamic and powerful environment is an intrinsic uncertainty and low trustworthiness due to its opaque perimeter, the multitude of different data sources with unknown providers, and uncertain responsibilities. Trustworthiness of observables collected by smart devices (from minuscle sensors to bigger machines) is fundamental to build a chain of trust on a decision process taken according to these observables. Some assurance solutions evaluate the quality of collected data, though they are difficult to apply in IoT environments for performance and cost reasons. In this paper, we take a different approach and put forward the idea that, in many cases, the behavior of people owning smart devices can contribute to the evaluation of the trustworthiness of collected data and, in turn, of the whole decision process. We therefore define an assurance methodology based on data analytics evaluating the compliance of people to behavioral policies. The more people behavior is compliant, the higher the trustworthiness of data collected through their smart devices.

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A novel data auditing approach to achieve data privacy and data integrity in cloud computing

Cited by 20 publications

References 7 publications

An Enhanced Inference Algorithm for Data Sampling Efficiency and Accuracy Using Periodic Beacons and Optimization

An Enhanced Inference Algorithm for Data Sampling Efficiency and Accuracy Using Periodic Beacons and Optimization

Fuzzy-Taylor-elephant herd optimization inspired Deep Belief Network for DDoS attack detection and comparison with state-of-the-arts algorithms

A trust assurance technique for Internet of things based on human behavior compliance

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