CRJan 14, 2012

Analysis of Different Privacy Preserving Cloud Storage Frameworks

arXiv:1205.2738v19 citations
Originality Synthesis-oriented
AI Analysis

This is an incremental analysis for researchers or practitioners interested in cloud storage security, focusing on evaluating existing methods.

The paper analyzes two existing privacy-preserving cloud storage frameworks, comparing their feasibility, running overhead, and privacy security, but does not present new results or concrete numbers.

Privacy Security of data in Cloud Storage is one of the main issues. Many Frameworks and Technologies are used to preserve data security in cloud storage. [1] Proposes a framework which includes the design of data organization structure, the generation and management of keys, the treatment of change of user's access right and dynamic operations of data, and the interaction between participants. It also design an interactive protocol and an extirpation-based key derivation algorithm, which are combined with lazy revocation, it uses multi-tree structure and symmetric encryption to form a privacy-preserving, efficient framework for cloud storage. [2] Proposes a framework which design a privacy-preserving cloud storage framework in which he designed an interaction protocol among participants, use key derivation algorithm to generate and manage keys, use both symmetric and asymmetric encryption to hide the sensitive data of users, and apply Bloom filter for cipher text retrieval. A system based on this framework is realized. This paper analyzes both the frameworks in terms of the feasibility of the frameworks, running overhead of the system and the privacy security of the frameworks.

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