A Privacy Leakage Upper-Bound Constraint-Based Approach for Cost-Effective Privacy Preserving of Intermediate Datasets in Cloud
A Privacy Leakage Upper-Bound Constraint Based Approach for Cost-Effective Privacy Preserving of Intermediate Datasets In cloud
ABSTRACT
Cloud computing provides massive computation power and storage capacity which enable users to deploy computation and data intensive applications without infrastructure investment. Along the processing of such applications, a large volume of intermediate datasets will be generated, and often stored to save the cost of re-computing them. However, preserving the privacy of intermediate datasets becomes a challenging problem because adversaries may recover privacy-sensitive information by analyzing multiple intermediate datasets. Encrypting ALL datasets in cloud is widely adopted in existing approaches to address this challenge. But we argue that encrypting all intermediate datasets are neither efficient nor cost-effective because it is very time consuming and costly for data-intensive applications to en/decrypt datasets frequently while performing any operation on them. In this paper, we propose a novel upper-bound privacy leakage constraint based approach to identify which intermediate datasets need to be encrypted and which do not, so that privacy-preserving cost can be saved while the privacy requirements of data holders can still be satisfied. Evaluation results demonstrate that the privacy-preserving cost of intermediate datasets can be significantly reduced with our approach over existing ones where all datasets are encrypted.
EXISTING SYSTEM:
Existing technical approaches for preserving the priva-cy of datasets stored in cloud mainly include encryption and anonymization. On one hand, encrypting all datasets, a straightforward and effective approach, is widely adopted in current research . However, processing on encrypted datasets efficiently is quite a challenging task, because most existing applications only run on unencrypted datasets. However, preserving the privacy of intermediate datasets becomes a
ABSTRACT
Cloud computing provides massive computation power and storage capacity which enable users to deploy computation and data intensive applications without infrastructure investment. Along the processing of such applications, a large volume of intermediate datasets will be generated, and often stored to save the cost of re-computing them. However, preserving the privacy of intermediate datasets becomes a challenging problem because adversaries may recover privacy-sensitive information by analyzing multiple intermediate datasets. Encrypting ALL datasets in cloud is widely adopted in existing approaches to address this challenge. But we argue that encrypting all intermediate datasets are neither efficient nor cost-effective because it is very time consuming and costly for data-intensive applications to en/decrypt datasets frequently while performing any operation on them. In this paper, we propose a novel upper-bound privacy leakage constraint based approach to identify which intermediate datasets need to be encrypted and which do not, so that privacy-preserving cost can be saved while the privacy requirements of data holders can still be satisfied. Evaluation results demonstrate that the privacy-preserving cost of intermediate datasets can be significantly reduced with our approach over existing ones where all datasets are encrypted.
EXISTING SYSTEM:
Existing technical approaches for preserving the priva-cy of datasets stored in cloud mainly include encryption and anonymization. On one hand, encrypting all datasets, a straightforward and effective approach, is widely adopted in current research . However, processing on encrypted datasets efficiently is quite a challenging task, because most existing applications only run on unencrypted datasets. However, preserving the privacy of intermediate datasets becomes a