Several Layer Privacy Preserving Using Hash Solomon Algorithm

EasyChair Preprint 11920

Abstract

In the face of the exponential surge in unstructured

data, the landscape of cloud storage technology is undergoing

rapid evolution. Cloud service providers maintain a hands-off

approach, refraining from making recommendations regarding

the content or specific locations of stored data. The crux of

privacy protection strategies lies in the robust foundation of

encryption technology, and the spectrum of mechanisms available

for securing cloud storage is continually expanding.Our innova-

tive solution proposes a sophisticated, cloud-based multi-tiered

storage architecture, strategically designed to fortify the defense

of sensitive information while extracting the full potential of cloud

storage capabilities. Employing String Slicing as a foundational

element, we intricately break down information into manage-

able chunks. This process is complemented by an AI-driven

algorithm that dynamically assesses the relative importance of

cloud, cloud1, and local machine storage, optimizing the storage

landscape.What sets our solution apart is the integration of

an additional layer of privacy preservation, achieved through

the implementation of the Solomon algorithm within a multi-

layered framework that harnesses hash functions. This metic-

ulous approach not only enhances security but also ensures a

comprehensive and dynamic privacy-preserving strategy.

Keyphrases: AI-driven algorithm, Comprehensive privacy strategy, Multi-tiered storage architecture, Solomon algorithm, cloud 1, hash functions, local machine storage, unstructured data

@booklet{EasyChair:11920,
  author    = {Sp Shreeja and S Snehashree and R Nivedha},
  title     = {Several Layer Privacy Preserving Using Hash Solomon Algorithm},
  howpublished = {EasyChair Preprint 11920},
  year      = {EasyChair, 2024}}