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On Deletion of Outsourced Data in Cloud Computing

Authors: Zhen Mo; Qingjun Xiao; Yian Zhou; Shigang Chen;

On Deletion of Outsourced Data in Cloud Computing

Abstract

Data security is a major concern in cloud computing. After clients outsource their data to the cloud, will they lose control of the data? Prior research has proposed various schemes for clients to confirm the existence of their data on the cloud servers, and the goal is to ensure data integrity. This paper investigates a complementary problem: When clients delete data, how can they be sure that the deleted data will never resurface in the future if the clients do not perform the actual data removal themselves? How to confirm the non-existence of their data when the data is not in their possession? One obvious solution is to encrypt the outsourced data, but this solution has a significant technical challenge because a huge amount of key materials may have to be maintained if we allow fine-grained deletion. In this paper, we explore the feasibility of relieving clients from such a burden by outsourcing keys (after encryption) to the cloud. We propose a novel multi-layered key structure, called Recursively Encrypted Red-black Key tree (RERK), that ensures no key materials will be leaked, yet the client is able to manipulate keys by performing tree operations in collaboration with the servers. We implement our solution on the Amazon EC2. The experimental results show that our solution can efficiently support the deletion of outsourced data in cloud computing.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
14
Top 10%
Top 10%
Average
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