Big Data Storage in Clouds project in python

A Secure and Verifiable Access Control Scheme for Big Data Storage in Clouds in python

PROJECT ID: PYTHON32

PROJECT NAME: A Secure and Verifiable Access Control Scheme for Big Data Storage in Clouds in python

 PROJECT CATEGORY: MCA / BCA / BCCA / MCM / POLY / ENGINEERING

 PROJECT ABSTRACT:

Due to the complexity and volume, outsourcing cipher texts to a cloud is deemed to be one of the most effective approaches for big data storage and access. Nevertheless, verifying the access legitimacy of a user and securely updating a cipher text in the cloud based on a new access policy designated by the data owner are two critical challenges to make cloud-based big data storage practical and effective. Traditional approaches either completely ignore the issue of access policy update or delegate the update to a third party authority; but in practice, access policy update is important for enhancing security and dealing with the dynamism caused by user join and leave activities. In this paper, we propose a secure and verifiable access control scheme based on the NTRU cryptosystem for big data storage in clouds. We first propose a new NTRU decryption algorithm to overcome the decryption failures of the original NTRU, and then detail our scheme and analyze its correctness, security strengths, and computational efficiency. Our scheme allows the cloud server to efficiently update the cipher text when a new access policy is specified by the data owner, who is also able to validate the update to counter against cheating behaviors of the cloud.

EXISTING SYSTEM

1.      BIG data is a high volume, and/or high velocity, high variety information asset, which requires new forms of processing to enable enhanced decision making, insight discovery, and process optimization

2.      Due to its complexity and large volume, managing big data using on hand database management tools is difficult. An effective solution is to outsource the data to a cloud server that has the capabilities of storing big data and processing users’ access requests in an efficient manner

3.      Most existing approaches for securing the outsourced big data in clouds are based on either attributed-based encryption (ABE) or secret sharing. ABE based approaches provide the flexibility for a data owner to predefine the set of users who are eligible for accessing the data but they suffer from the high complexity of efficiently updating the access policy and cipher text.

4.      As a data owner typically does not backup its data locally after outsourcing the data to a cloud, it cannot easily manage the data stored in the cloud.

5.      Besides, as more and more companies and organizations are using clouds to store their data, it becomes more challenging and critical to deal with the issue of access policy update for enhancing security and dealing with the dynamism

6.      Caused by the users’ join and leave activities. To the best of our knowledge, policy update for outsourced big data storage in clouds has never been considered by the existing research.

DISADVATAGES

1.      Existing schemes doesn’t support user eligibility verification. On the other hand, verifiable secret sharing based schemes rely on RSA for access legitimacy verification.

2.      As multiple users need to mutually verify each other using multiple RSA operations, such a procedure has a high computational overhead

3.      This is not a science fiction as in 2015 IBM brought quantum computing closer to reality, making it urgent to exploit new techniques for quantum computing attack resistance

4.      The Existing scheme should be able to defend against various attacks such as the collusion attack.

5.      Verification Problem it verified by other participating users

6.      To reduce the risk of information leakage, a user should obtain authorization from the data owner for accessing the encrypted data

PROPOSED SYSTEM

We first propose an improved NTRU cryptosystem to overcome the decryption failures of the original NTRU. Then we design a secure and verifiable scheme based on the improved NTRU and secret sharing for big data storage. The cloud server can directly update the stored cipher text without decryption based on the new access policy specified by the data owner, who is able to validate the update at the cloud. The proposed scheme can verify the shared secret information to prevent users from cheating and can counter various attacks such as the collusion attack. It is also deemed to be secure with respect to quantum computing attacks due to NTRU.

ADVANTAGES

1.      We propose a new NTRU decryption procedure to overcome the decryption failures of the original NTRU without reducing the security strength of NTRU

2.      We propose a secure and verifiable access control scheme to protect the big data stored in a cloud.

3.      The scheme can verify a user’s access legitimacy and validate the information provided by other users for correct plaintext recovery

4.      We devise an efficient and verifiable method to update the cipher text stored in clouds without increasing any risk when the access policy is dynamically changed by the data owner for various reasons

5.      We prove the correctness of the proposed scheme and investigate its efficiency and security strength.

6.      We demonstrate that our scheme can resist various attacks such as the collusion attack via a rigorous analysis.

SOFTWARE REQUIREMENTS:

OS                                : Windows

Python IDE                  : Python 2.7.x and above

Language                             : Python Programming

Database                             : MYSQL

 

HARDWARE REQUIREMENTS:

 RAM                :  4GB and Higher

Processor          :  Intel i3 and above

Hard Disk         : 500GB Minimum

 

Setting up Software Environment

Python is a high level, interpreted, interactive and object-oriented scripting language.

Python is designed to be highly readable and has fewer syntactical constructions than other languages. Python is used in the development of this model. In this experiment, the following python libraries are used to develop the machine learning models:

• NLTK: It is a python package which works with human language data and provides an easy-to-use interface to different lexical resources like WordNet and text processing libraries. These lexical resources are used for classification, tokenization, stemming, tagging, parsing, and semantic reasoning.

• Pandas: It is a python package which acts as a data analysis tool and deals with data structures. Pandas carry out entire data analysis workflow in Python without having to switch to a more domain specific language like R.

• Tweepy: It is used in accessing the Twitter API by establishing the connection and to gather tweets from Twitter. 

This module is used to stream live tweets directly from Twitter in real-time.

• Numpy: NumPy is the fundamental package for computing with Python. It is used to add support to multi-dimensional arrays and matrices, with a large collection of high-level mathematical functions.

• scikit-learn: It is a simple and efficient tool for data mining and data analysis. 

• matplotlib python library which generates plots, histograms, power spectra, bar charts, etc.

In this work matplotlib.pyplot module is used to plot the metrics.

• Gensim It is used to automatically extract semantic topics from documents, as efficiently as possible. Gensim is designed to process raw, unstructured text data. The algorithms in Gensim, such as Word2Vec where it automatically discovers the semantic structure of phrase by examining statistical co-occurrence patterns within a corpus of training documents. These algorithms are unsupervised. Once these statistical patterns are found, any plain text documents can be succinctly expressed in the new, semantic representation and queried for topical similarity against other documents.

• Keras: Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

 TABLE OF CONTENTS

·        Title Page      

·        Declaration

·        Certification Page

·        Dedication

·        Acknowledgements

·        Table of Contents

·        List of Tables

·        Abstract

CHAPTER SCHEME

CHAPTER ONE: INTRODUCTION

CHAPTER TWO: OBJECTIVES

CHAPTER THREE: PRELIMINARY SYSTEM ANALYSIS

·         Preliminary Investigation

·         Present System in Use

·         Flaws In Present System

·         Need Of New System

·         Feasibility Study

·         Project  Category

CHAPTER FOUR: SOFTWARE ENGINEERING AND PARADIGM APPLIED   

·         Modules

·         System / Module Chart

CHAPTER FIVE: SOFTWARE AND HARDWARE REQUIREMENT

CHAPTER SIX: DETAIL SYSTEM ANALYSIS

·         Data Flow Diagram

·         Number of modules and Process Logic

·         Data Structures  and Tables

·         Entity- Relationship Diagram

·         System Design

·         Form Design 

·         Source Code

·         Input Screen and Output Screen

CHAPTER SEVEN: TESTING AND VALIDATION CHECK

CHAPTER EIGHT: SYSTEM SECURITY MEASURES

CHAPTER NINE: IMPLEMENTATION, EVALUATION & MAINTENANCE

CHAPTER TEN: FUTURE SCOPE OF THE PROJECT

CHAPTER ELEVEN: SUGGESTION AND CONCLUSION

CHAPTER TWELE: BIBLIOGRAPHY& REFERENCES          

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