A Privacy Leakage Upper-bound Constraint based Approach for Cost-effective Privacy Preserving of Intermediate Datasets in Cloud
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Year of Project2013
A Privacy Leakage Upper-bound Constraint based Approach for Cost-effective Privacy Preserving of Intermediate Datasets in Cloud IEEE Projects 2013 | Final year projects | BE Projects | 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 data sets will be generated, and often stored to save the cost of recomputing them. However, preserving the privacy of intermediate data sets becomes a challenging problem because adversaries may recover privacy-sensitive information by analyzing multiple intermediate data sets. Encrypting ALL data sets in cloud is widely adopted in existing approaches to address this challenge. But we argue that encrypting all intermediate data sets are neither efficient nor cost-effective because it is very time consuming and costly for data-intensive applications to en/decrypt data sets 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 data sets 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 data sets can be significantly reduced with our approach over existing ones where all data sets are encrypted.
TECHNICALLY, cloud computing is regarded as an ingenious combination of a series of technologies, establishing a novel business model by offering IT services and using economies of scale. Participants
in the business chain of cloud computing can benefit from this novel model. Cloud customers can save huge capital investment of IT infrastructure, and concentrate on their own core business . Therefore, many companies or organizations have been migrating or building their business into cloud. However, numerous potential customers are still hesitant to take advantage of cloud due to security and privacy concerns.
The privacy concerns caused by retaining intermediate datasets in cloud are important but they are paid little attention. Storage and computation services in cloud are equivalent from an economical perspective because they are charged in proportion to their usage. Thus, cloud users can store valuable intermediate datasets selectively when processing original datasets in data-intensive applications like medical diagnosis, in order to curtail the overall expenses by avoiding frequent re-computation to obtain these datasets. Such scenarios are quite common because data users often re-analyze results, conduct new analysis on intermediate datasets, or share some intermediate results with others for collaboration. Without loss of generality, the notion of intermediate dataset herein refers to intermediate and resultant datasets. However, the storage of intermediate data enlarges attack surfaces so that privacy requirements of data holders are at risk of being violated. Usually, intermediate datasets in cloud are accessed and processed by multiple parties, but
rarely controlled by original dataset holders. This enables an adversary to collect intermediate datasets together and menace privacy-sensitive information from them, bringing considerable economic loss or severe social reputation impairment to data owners. But little attention has been paid to such a cloud-specific privacy issue.
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