Sanitizable Access Control System for Secure Cloud Storage Against Malicious Data Publishers

Project Code :TCMAPY1021

Objective

Cloud computing offers cost-effective IT solutions but raises security concerns. Storing plaintext data poses threats, necessitating encryption. Traditional protection fails against malicious data publishers. This research introduces the Sanitizable Access Control System (SACS) for secure cloud storage resistant to such publishers, leveraging q-Parallel Bilinear Diffie-Hellman Exponent Assumption.

Abstract

Cloud computing, while cost-effective, faces security challenges from malicious data publishers. Existing solutions focus on controlling access for legitimate users but neglect the threat posed by these malicious entities. Addressing this gap, our research introduces the concept of a Sanitizable Access Control System (SACS) for secure cloud storage. SACS, based on q-Parallel Bilinear Diffie-Hellman Exponent Assumption, safeguards against unauthorized decryption attempts by malicious data publishers. Our work defines a threat model, formal security measures, and presents a secure design with a security proof. This pioneering approach fills a practical research gap, enhancing the practical adoption of secure cloud storage amid evolving security concerns.

Keywordβ€”Secure cloud storage, access control, sanitizable, malicious data publishers.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

H/W CONFIGURATION:

Processor - I3/Intel Processor

Hard Disk - 160GB

Key Board - Standard Windows Keyboard

Mouse - Two or Three Button Mouse

Monitor - SVGA

RAM - 8GB


S/W CONFIGURATION:

β€’ Operating System :  Windows 7/8/10

β€’ Server side Script :  HTML, CSS, Bootstrap & JS

β€’ Programming Language :  Python

β€’ Libraries :  Django/flask, Pandas, Mysql.connector, Os, Smtplib, Numpy

β€’ IDE/Workbench :  PyCharm

β€’ Technology :  Python 3.6+

β€’ Server Deployment :  Xampp Server


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