Misusability Measure Based Sanitization of Big Data for Privacy Preserving MapReduce Programming

Radhika, D.; Kumari, D. Aruna

doi:10.11591/ijece.v8i6.pp4524-4532

Cited by 3 publications

(8 citation statements)

References 16 publications

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“…Many big companies like Google, Tesla and others are investing too much in self-driving cars. Tesla cars are already equipped with autopilot feature which uses a system of 8 cameras and 12 sensors which provides 360 degree view to a range of 250 meters [32]. Education ML helps to organize and optimize the content module and track student knowledge and recommend next step.…”

Section: Applications Of Machine Learning Electrical Power and Energymentioning

confidence: 99%

A survey of big data and machine learning

Salkuti¹

2020

IJECE

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This paper presents a detailed analysis of big data and machine learning (ML) in the electrical power and energy sector. Big data analytics for smart energy operations, applications, impact, measurement and control, and challenges are presented in this paper. Big data and machine learning approaches need to be applied after analyzing the power system problem carefully. Determining the match between the strengths of big data and machine learning for solving the power system problem is of utmost important. They can be of great help to plan and operate the traditional grid/smart grid (SG). The basics of big data and machine learning are described in detailed manner along with their applications in various fields such as electrical power and energy, health care and life sciences, government, telecommunications, web and digital media, retailers, finance, e-commerce and customer service, etc. Finally, the challenges and opportunities of big data and machine learning are presented in this paper.

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Section: Applications Of Machine Learning Electrical Power and Energymentioning

confidence: 99%

A survey of big data and machine learning

Salkuti¹

2020

IJECE

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“…The framework discussed in this section was actually proposed in our prior work [2]. However, it is briefly discussed here prior to evaluating the misuse probability based big data sanitization approach.…”

Section: Proposed Frameworkmentioning

confidence: 99%

“…Thus the outsourced data is to be subjected to data analytics for business intelligence. Such data is prone to various kinds of inference attacks [2]. Therefore, there is need for protecting privacy of big data.…”

Section: Introductionmentioning

confidence: 99%

“…This is very important hypothesis that is not only proved in this paper but also the trade-off between misuse probability of big data and sanitization are analysed. To be more precise, in this paper we propose a misuse probability score measure (also found in our prior work [2]) that is the basis for determining appropriate sanitization which strikes balance between utility and privacy of big data. Big data might have multiple dimensions.…”

Section: Introductionmentioning

confidence: 99%

“…3 We implemented the framework with cloud environment that includes Amazon EC2, Amazon EMR and Amazon S3. The framework is evaluated with standard measures like precision, recall, RMSE and classification Accuracy using parallel k-anonymity (MapReduce model) algorithm defined in our prior work [1] and MapReduce based classification algorithm [2].…”

Section: Introductionmentioning

confidence: 99%

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One Size Does Not Fit All: Trade-offs between Misuse Probability and Level of Sanitization for Big Data

2019

IJRTE

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Big data privacy has assumed importance as the cloud computing became a phenomenal success in providing a remote platform for sharing computing resources without geographical and time restrictions. However, the privacy concerns on the big data being outsourced to public cloud storage are still exist. Different anonymity or sanitization techniques came into existence for protecting big data from privacy attacks. In our prior works, we have proposed a misusability probability based metric to know the probable percentage of misusability. We additionally planned a system that suggests level of sanitization before actually applying privacy protection to big data. It was based on misusability probability. In this paper, our focus is on further evaluation of our misuse probability based sanitization of big data approach by defining an algorithm which willanalyse the trade-offs between misuse probability and level of sanitization. It throws light into the proposed framework and misusability measure besides evaluation of the framework with an empirical study. Empirical study is made in public cloud environment with Amazon EC2 (compute engine), S3 (storage service) and EMR (MapReduce framework). The experimental results revealed the dynamics of the trade-offs between them. The insights help in making well informed decisions while sanitizing big data to ensure that it is protected without losing utility required.

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Optimized memory model for hadoop map reduce framework

Bhaskar¹,

Ranjan

2019

IJECE

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Map Reduce is the preferred computing framework used in large data analysis and processing applications. Hadoop is a widely used Map Reduce framework across different community due to its open source nature. Cloud service provider such as Microsoft azure HDInsight offers resources to its customer and only pays for their use. However, the critical challenges of cloud service provider is to meet user task Service level agreement (SLA) requirement (task deadline). Currently, the onus is on client to compute the amount of resource required to run a job on cloud. This work present a novel memory optimization model for Hadoop Map Reduce framework namely MOHMR (Optimized Hadoop Map Reduce) to process data in real-time and utilize system resource efficiently. The MOHMR present accurate model to compute job memory optimization and also present a model to provision the amount of cloud resource required to meet task deadline. The MOHMR first build a profile for each job and computes memory optimization time of job using greedy approach. Experiment are conducted on Microsoft Azure HDInsight cloud platform considering different application such as text computing and bioinformatics application to evaluate performance of MOHMR of over existing model shows significant performance improvement in terms of computation time. Experiment are conducted on Microsoft Azure HDInsight cloud. Overall, good correlation is reported between practical memory optimization values and theoretical memory optimization values.

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Misusability Measure Based Sanitization of Big Data for Privacy Preserving MapReduce Programming

Cited by 3 publications

References 16 publications

A survey of big data and machine learning

A survey of big data and machine learning

One Size Does Not Fit All: Trade-offs between Misuse Probability and Level of Sanitization for Big Data

Optimized memory model for hadoop map reduce framework

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