Cryptanalysis and Improvement of a Provable Data Possession Scheme in Public Cloud Storage

2016 ◽  
Author(s):  
Tung-Tso Tsai ◽  
Yuh-Min Tseng ◽  
Ying-Hao Hung ◽  
Sen-Shan Huang
Keyword(s):  
Cloud Storage ◽  
Public Cloud ◽  
Provable Data Possession

A New Delegation Provable Data Possession in Public Cloud Storage

2014 ◽  
Vol 644-650 ◽  
pp. 2239-2244
Author(s):  
Bin Li ◽  
Chen Lei Cao ◽  
Jian Yi Liu ◽  
Jin Xia Wei
Keyword(s):  
Performance Analysis ◽  
Cloud Storage ◽  
Security Analysis ◽  
Data Integrity ◽  
Public Cloud ◽  
Provable Data Possession ◽  
Integrity Verification ◽  
And Performance ◽  
Remote Data

Though Cloud storage has developed rapidly in recent years, there still exist some problems obviously. Provable Data Possession (PDP) is proposed to solve the problem of data integrity verification at untrusted cloud stores. This study built a new delegation Provable Data Possession (delegation-PDP), which solves problem when the client has no ability to check its remote data. We study the delegation-PDP and use proxy re-encryption to design it. Then we use the improved Elgamal-based algorithm to implement the scheme. Through security analysis and performance analysis, our protocol is provable secure and efficient.

Role of acquisition intervals in private and public cloud storage costs

2014 ◽  
Vol 57 ◽  
pp. 320-330 ◽  
Author(s):  
Gabriella Laatikainen ◽  
Oleksiy Mazhelis ◽  
Pasi Tyrväinen
Keyword(s):  
Cloud Storage ◽  
Public Cloud ◽  
Private And Public

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

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3606-3611
Keyword(s):  
Big Data ◽  
Empirical Study ◽  
Cloud Storage ◽  
Data Privacy ◽  
Public Cloud ◽  
Mapreduce Framework ◽  
Privacy Concerns ◽  
Time Restrictions ◽  
Trade Offs ◽  
Made In

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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