Privacy preservation using hybrid cloud environment and Map-Reduce for data deduplication

2019 ◽  
Vol 14 (3/4) ◽  
pp. 281
Author(s):  
Rutuja Dadasaheb Mote ◽  
Ambika Vishal Pawar
Keyword(s):  
Privacy Preservation ◽  
Map Reduce ◽  
Hybrid Cloud ◽  
Cloud Environment ◽  
Data Deduplication

Big Data Privacy Preservation Using Two Phase Top-Down Specialization Algorithm with Multidimensional Map Reduce Framework on Hadoop

2015 ◽  
Vol 3 (2) ◽  
Author(s):  
Shalin Eliabeth S. ◽  
Sarju S.
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Experimental Result ◽  
Map Reduce ◽  
Distributed Environment ◽  
Top Down ◽  
Two Phase ◽  
Data Anonymization ◽  
Big Data Privacy

Big data privacy preservation is one of the most disturbed issues in current industry. Sometimes the data privacy problems never identified when input data is published on cloud environment. Data privacy preservation in hadoop deals in hiding and publishing input dataset to the distributed environment. In this paper investigate the problem of big data anonymization for privacy preservation from the perspectives of scalability and time factor etc. At present, many cloud applications with big data anonymization faces the same kind of problems. For recovering this kind of problems, here introduced a data anonymization algorithm called Two Phase Top-Down Specialization (TPTDS) algorithm that is implemented in hadoop. For the data anonymization-45,222 records of adults information with 15 attribute values was taken as the input big data. With the help of multidimensional anonymization in map reduce framework, here implemented proposed Two-Phase Top-Down Specialization anonymization algorithm in hadoop and it will increases the efficiency on the big data processing system. By conducting experiment in both one dimensional and multidimensional map reduce framework with Two Phase Top-Down Specialization algorithm on hadoop, the better result shown in multidimensional anonymization on input adult dataset. Data sets is generalized in a top-down manner and the better result was shown in multidimensional map reduce framework by the better IGPL values generated by the algorithm. The anonymization was performed with specialization operation on taxonomy tree. The experiment shows that the solutions improves the IGPL values, anonymity parameter and decreases the execution time of big data privacy preservation by compared to the existing algorithm. This experimental result will leads to great application to the distributed environment.

scholarly journals An IoT-Oriented data placement method with privacy preservation in cloud environment

2018 ◽  
Vol 124 ◽  
pp. 148-157 ◽  
Author(s):  
Xiaolong Xu ◽  
Shucun Fu ◽  
Lianyong Qi ◽  
Xuyun Zhang ◽  
Qingxiang Liu ◽  
...  
Keyword(s):  
Privacy Preservation ◽  
Data Placement ◽  
Cloud Environment ◽  
Placement Method

scholarly journals Deduplication in Cloud Storage

2019 ◽  
Vol 9 (2S) ◽  
pp. 364-368
Keyword(s):  
Data Storage ◽  
Cloud Storage ◽  
Cloud Service ◽  
Information Storage ◽  
Cloud Environment ◽  
Data Deduplication ◽  
Cloud Service Provider ◽  
Level Data ◽  
Block Level ◽  
Individual Client

Cloud Computing is well known today on account of enormous measure of data storage and quick access of information over the system. It gives an individual client boundless extra space, accessibility and openness of information whenever at anyplace. Cloud service provider can boost information storage by incorporating data deduplication into cloud storage, despite the fact that information deduplication removes excess information and reproduced information happens in cloud environment. This paper presents a literature survey alongside different deduplication procedures that have been based on cloud information storage. To all the more likely guarantee secure deduplication in cloud, this paper examines file level data deduplication and block level data deduplication.

scholarly journals How to Achieve Compliance with GDPR Article 17 in a Hybrid Cloud Environment

Sci ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 22
Author(s):  
Miriam Kelly ◽  
Eoghan Furey ◽  
Kevin Curran
Keyword(s):  
Ad Hoc ◽  
Data Repository ◽  
Hybrid Cloud ◽  
Cloud Environment ◽  
Cloud Infrastructure ◽  
General Data Protection Regulation ◽  
Right To Be Forgotten ◽  
Personally Identifiable Information ◽  
General Data ◽  
The Right

On 25 May 2018, the General Data Protection Regulation (GDPR)Article 17, the Right to Erasure (‘Right to be Forgotten’) came into force making it vital for organisations to identify, locate and delete all Personally Identifiable Information (PII) where a valid request is received from a data subject to erase their PII and the contractual period has expired. This must be done without undue delay and the organisation must be able to demonstrate reasonable measures were taken. Failure to comply may incur significant fines, not to mention impact to reputation. Many organisations do not understand their data, and the complexity of a hybrid cloud infrastructure means they do not have the resources to undertake this task. The variety of available tools are quite often unsuitable as they involve restructuring so there is one centralised data repository. This research aims to demonstrate compliance with GDPR’s Article 17 Right to Erasure (‘Right to be Forgotten’) is achievable in a Hybrid cloud environment by following a list of recommendations. However, 100% retrieval, 100% of time will not be possible, but we show that small organisations running an ad-hoc Hybrid cloud environment can demonstrate that reasonable measures were taken to be Right to Erasure (‘Right to be Forgotten’) compliant.

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