Distributed Differential Evolution for Anonymity-Driven Vertical Fragmentation in Outsourced Data Storage

2020 ◽  
pp. 213-226
Author(s):  
Yong-Feng Ge ◽  
Jinli Cao ◽  
Hua Wang ◽  
Yanchun Zhang ◽  
Zhenxiang Chen
Keyword(s):  
Differential Evolution ◽  
Data Storage ◽  
Vertical Fragmentation ◽  
Outsourced Data

scholarly journals Set-Based Adaptive Distributed Differential Evolution for Anonymity-Driven Database Fragmentation

2021 ◽  
Author(s):  
Yong-Feng Ge ◽  
Jinli Cao ◽  
Hua Wang ◽  
Zhenxiang Chen ◽  
Yanchun Zhang
Keyword(s):  
Differential Evolution ◽  
Prior Knowledge ◽  
Data Storage ◽  
Population Diversity ◽  
Mutation Operator ◽  
Outsourced Data ◽  
Mutation Strategy ◽  
Discrete Domain ◽  
Continuous Domain ◽  
Better Than

AbstractBy breaking sensitive associations between attributes, database fragmentation can protect the privacy of outsourced data storage. Database fragmentation algorithms need prior knowledge of sensitive associations in the tackled database and set it as the optimization objective. Thus, the effectiveness of these algorithms is limited by prior knowledge. Inspired by the anonymity degree measurement in anonymity techniques such as k-anonymity, an anonymity-driven database fragmentation problem is defined in this paper. For this problem, a set-based adaptive distributed differential evolution (S-ADDE) algorithm is proposed. S-ADDE adopts an island model to maintain population diversity. Two set-based operators, i.e., set-based mutation and set-based crossover, are designed in which the continuous domain in the traditional differential evolution is transferred to the discrete domain in the anonymity-driven database fragmentation problem. Moreover, in the set-based mutation operator, each individual’s mutation strategy is adaptively selected according to the performance. The experimental results demonstrate that the proposed S-ADDE is significantly better than the compared approaches. The effectiveness of the proposed operators is verified.

scholarly journals Overview of current techniques in remote data auditing

2016 ◽  
Vol 1 (1) ◽  
pp. 145-158 ◽  
Author(s):  
Hualong Wu ◽  
Bo Zhao
Keyword(s):  
Data Storage ◽  
Erasure Coding ◽  
Network Access ◽  
Data Outsourcing ◽  
Local Data ◽  
Cloud Data ◽  
Resource Pooling ◽  
Outsourced Data ◽  
Data Auditing ◽  
Remote Data

AbstractThe emergence of cloud computing brings the infinite imagination space, both in individual and organizations, due to its unprecedented advantages in the IT history: on-demand self-service, ubiquitous network access, location independent resource pooling, rapid resource elasticity, usage-based pricing and transference of risk. Many of the individuals or organizations ease the pressure on their local data storage, and mitigate the maintenance overhead of local data storage by using outsource data to cloud. However, the data outsourcing is not absolutely safe in the cloud. In order to enhance the users’ confidence of the integrity of their outsource data in the cloud. To promote the rapid deployment of cloud data storage service and regain security assurances with outsourced data dependability, many scholars tend to design the Remote Data Auditing (RDA) technique as a new concept to enable public auditability for the outsourced data in the cloud. The RDA is a useful technique to ensure the correctness of the data outsourced to cloud servers. This paper presents a comprehensive survey on techniques of remote data auditing in cloud server. Recently, more and more remote auditing approaches are categorized into the three different classes, that is, replication-based, erasure coding-based, and network coding-based to present a taxonomy. This paper also aims to the explore major issues.

scholarly journals TrustedDW: A New Framework to Securely Hosting Data Warehouse in the Cloud

10.29007/q3wd ◽  
2019 ◽  
Author(s):  
Kawthar Karkouda ◽  
Ahlem Nabli ◽  
Faiez Gargouri
Keyword(s):  
Cloud Computing ◽  
Data Warehouse ◽  
Data Storage ◽  
Homomorphic Encryption ◽  
New Technology ◽  
Sensitive Data ◽  
It Industry ◽  
Security Issues ◽  
Outsourced Data ◽  
Storage Network

Nowadays cloud computing become the most popular technology in the area of IT industry. It provides computing power, storage, network and software as a service. While building, a data warehouse typically necessitates an important initial investment. With the cloud pay-as-you-go model, BI system can benefit from this new technology. But, as every new technology, cloud computing brings its own risks in term of security. Because some security issues are inherited from classical architectures, some traditional security solutions are used to protect outsourced data. Unfortunately, those solutions are not enough and cannot guarantee the privacy of sensitive data hosted in the Cloud. In particular, in the case of data warehouse, using traditional encryption solutions cannot be practical because those solutions induce a heavy overhead in terms of data storage and query performance. So, a suitable schema must be proposed in order to balance the security and the performance of data warehouse hosted in the cloud. In this paper, we propose (TrustedDW) a homomorphic encryption schema for securing and querying a data warehouse hosted in the cloud.

DStore: A distributed system for outsourced data storage and retrieval

2019 ◽  
Vol 99 ◽  
pp. 106-114
Author(s):  
Jingting Xue ◽  
Chunxiang Xu ◽  
Lanhua Bai
Keyword(s):  
Data Storage ◽  
Distributed System ◽  
Storage And Retrieval ◽  
Outsourced Data

scholarly journals A Secure and Effective Anonymous Integrity Checking Protocol for Data Storage in Multicloud

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Lingwei Song ◽  
Dawei Zhao ◽  
Xuebing Chen ◽  
Chenlei Cao ◽  
Xinxin Niu
Keyword(s):  
Data Storage ◽  
Cloud Storage ◽  
Third Party ◽  
Integrity Verification ◽  
Public Verifiability ◽  
Data Dynamics ◽  
Outsourced Data ◽  
Integrity Checking ◽  
Identity Based ◽  
Simulation Results

How to verify the integrity of outsourced data is an important problem in cloud storage. Most of previous work focuses on three aspects, which are providing data dynamics, public verifiability, and privacy against verifiers with the help of a third party auditor. In this paper, we propose an identity-based data storage and integrity verification protocol on untrusted cloud. And the proposed protocol can guarantee fair results without any third verifying auditor. The theoretical analysis and simulation results show that our protocols are secure and efficient.

Differential Evolution based bucket indexed data deduplication for big data storage

2018 ◽  
Vol 34 (1) ◽  
pp. 491-505 ◽  
Author(s):  
Naresh Kumar ◽  
Shobha Antwal ◽  
S.C. Jain
Keyword(s):  
Big Data ◽  
Differential Evolution ◽  
Data Storage ◽  
Data Deduplication ◽  
Big Data Storage
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