In This Application 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 Application 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 Application, 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
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