RECORD
cSELENE: Privacy Preserving Query Retrieval System on Heterogeneous Cloud Data
Databases · Submitted 2 May 2018
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Our experiments should demonstrate the validity and practicality of the proposed high performance computing scheme.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | While working in collaborative team elsewhere sometimes the federated (huge) data are from heterogeneous cloud vendors. | background 1.00 | 0.24 | 0.02 | asserted 0.83 |
| s1 | 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. | background 0.89 | 0.06 | 0.04 | asserted 0.92 |
| s2 | Previous solution offered hybrid cloud between public and trusted private cloud. | background 1.00 | 0.02 | 0.02 | asserted 1.00 |
| s3 | Another previous solution used encryption on MapReduce framework. | background 1.00 | 0.02 | 0.02 | asserted 1.00 |
| s4 | But the challenge is we are working on heterogeneous clouds. | gap 0.76 | 0.23 | 0.02 | asserted 0.99 |
| s5 | In this paper, we present a novel technique for querying with privacy concern. | contribution 1.00 | 0.76 | 0.18 | asserted 1.00 |
| s6 | 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. | method 0.83 | 0.55 | 0.14 | asserted 0.97 |
| s7 | By using model of the database it means we use only the summary or the very characteristic patterns of the database. | method 0.98 | 0.39 | 0.04 | asserted 1.00 |
| s8 | Modeling is the Preserving Privacy Stage I, since by modeling the data is being symbolized. | method 0.97 | 0.56 | 0.04 | asserted 1.00 |
| s9 | We implement encryption on the database as preserving privacy Stage II. | method 0.94 | 0.80 | 0.04 | asserted 1.00 |
| s10 | 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. | contribution 0.88 | 0.69 | 0.08 | asserted 1.00 |
| s11 | 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. | contribution 0.97 | 0.47 | 0.05 | asserted 0.98 |
| s12 | Other issues such as incremental method and the secure design of cloud architecture system (Virtual Machines across platform design) are still open to discuss. | future work 0.92 | 0.11 | 0.03 | asserted 0.60 |
| s13 | Our experiments should demonstrate the validity and practicality of the proposed high performance computing scheme. | future work 0.39 | 0.23 | 0.17 | hedged possibility 0.70 |
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The paper
Original abstract
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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