cSELENE: Privacy Preserving Query Retrieval System on Heterogeneous Cloud Data
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 hy...
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Zusammenfassung: | 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. |
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DOI: | 10.48550/arxiv.1805.01275 |