cSELENE: Privacy Preserving Query Retrieval System on Heterogeneous Cloud Data

by   Diyah Puspitaningrum, et al.

While working in collaborative team elsewhere sometimes the federated (huge) data are from heterogeneous cloud vendors. It is not only about the data privacy concern but also about how can those federated data can be querying from cloud directly in fast and securely way. Previous solution offered hybrid cloud between public and trusted private cloud. Another previous solution used encryption on MapReduce framework. But the challenge is we are working on heterogeneous clouds. In this paper, we present a novel technique for querying with privacy concern. Since we take execution time into account, our basic idea is to use the data mining model by partitioning the federated databases in order to reduce the search and query time. By using model of the database it means we use only the summary or the very characteristic patterns of the database. Modeling is the Preserving Privacy Stage I, since by modeling the data is being symbolized. We implement encryption on the database as preserving privacy Stage II. Our system, called "cSELENE" (stands for "cloud SELENE"), is designed to handle federated data on heterogeneous clouds: AWS, Microsoft Azure, and Google Cloud Platform with MapReduce technique. In this paper we discuss preserving-privacy system and threat model, the format of federated data, the parallel programming (GPU programming and shared/memory systems), the parallel and secure algorithm for data mining model in distributed cloud, the cloud infrastructure/architecture, and the UIX design of the cSELENE system. Other issues such as incremental method and the secure design of cloud architecture system (Virtual Machines across platform design) are still open to discuss. Our experiments should demonstrate the validity and practicality of the proposed high performance computing scheme.


page 1

page 2

page 3

page 4


Heal the Privacy: Functional Encryption and Privacy-Preserving Analytics

Secure cloud storage is an issue of paramount importance that both busin...

Privacy-Preserving Secret Shared Computations using MapReduce

Data outsourcing allows data owners to keep their data at untrusted clou...

MSPPIR: Multi-source privacy-preserving image retrieval in cloud computing

Content-Based Image Retrieval (CBIR) techniques have been widely researc...

Faster Secure Data Mining via Distributed Homomorphic Encryption

Due to the rising privacy demand in data mining, Homomorphic Encryption ...

Mining Privacy-Preserving Association Rules based on Parallel Processing in Cloud Computing

With the onset of the Information Era and the rapid growth of informatio...

VM Image Repository and Distribution Models for Federated Clouds: State of the Art, Possible Directions and Open Issues

The emerging trend of Federated Cloud models enlist virtualization as a ...

Preserving Data Confidentiality in Association Rule Mining Using Data Share Allocator Algorithm

These days, investigations of information are becoming essential for var...

Please sign up or login with your details

Forgot password? Click here to reset