Bloom Filter in Cloud Storage for Efficient Data Membership Identification

2021 ◽  
pp. 715-726
Author(s):  
Niraja Jain ◽  
B. Raghu ◽  
V. Khanaa
Keyword(s):  
Cloud Storage ◽  
Bloom Filter ◽  
Efficient Data

scholarly journals Efficient Data Possession Auditing for Real-World Cloud Storage Environments

2015 ◽  
Vol E98.D (4) ◽  
pp. 796-806 ◽  
Author(s):  
Da XIAO ◽  
Lvyin YANG ◽  
Chuanyi LIU ◽  
Bin SUN ◽  
Shihui ZHENG
Keyword(s):  
Real World ◽  
Cloud Storage ◽  
Efficient Data

scholarly journals Study of privacy-preserving framework for cloud storage

2011 ◽  
Vol 8 (3) ◽  
pp. 801-819 ◽  
Author(s):  
Huang Ruwei ◽  
Gui Xiaolin ◽  
Yu Si ◽  
Zhuang Wei
Keyword(s):  
Data Storage ◽  
Cloud Storage ◽  
Security Analysis ◽  
Bloom Filter ◽  
Data Access ◽  
Data Retrieval ◽  
Privacy Preserving ◽  
Index Structure ◽  
Structure Generation ◽  
Access Right

In order to implement privacy-preserving, efficient and secure data storage and access environment of cloud storage, the following problems must be considered: data index structure, generation and management of keys, data retrieval, treatments of change of users? access right and dynamic operations on data, and interactions among participants. To solve those problems, the interactive protocol among participants is introduced, an extirpation-based key derivation algorithm (EKDA) is designed to manage the keys, a double hashed and weighted Bloom Filter (DWBF) is proposed to retrieve the encrypted keywords, which are combined with lazy revocation, multi-tree structure, asymmetric and symmetric encryptions, which form a privacypreserving, efficient and secure framework for cloud storage. The experiment and security analysis show that EKDA can reduce the communication and storage overheads efficiently, DWBF supports ciphertext retrieval and can reduce communication, storage and computation overhead as well, and the proposed framework is privacy preserving while supporting data access efficiently.

An Efficient Data Fingerprint Query Algorithm Based on Two-Leveled Bloom Filter

2013 ◽  
Vol 8 (2) ◽  
Author(s):  
Bin Zhou ◽  
Rongbo Zhu ◽  
Ying Zhang ◽  
Linhui Cheng
Keyword(s):  
Bloom Filter ◽  
Efficient Data ◽  
Query Algorithm

scholarly journals VOTE: Verifiable Auditing for Outsourced Database with Token Enforced Cloud Storage

2019 ◽  
Vol 9 (2) ◽  
pp. 5156-5166
Keyword(s):  
Cloud Storage ◽  
Bloom Filter ◽  
Cloud Service ◽  
Communication Cost ◽  
Cloud Service Provider ◽  
Merkle Hash Tree ◽  
Database Administration ◽  
Outsourced Database ◽  
Counting Bloom Filter ◽  
Null Set

Database deploying is one of the remarkable utilities in cloud computing where the Information Proprietor (IP) assigns the database administration to the Cloud Service Provider (CSP) in order to lower the administration overhead and preservation expenditures of the database. Regardless of its overwhelming advantages, it experiences few security problems such as confidentiality of deployed database and auditability of search outcome. In recent past, survey has been carried out on the auditability of search outcome of deployed database that gives preciseness and intactness of search outcome. But in the prevailing schemes, since there is flow of data between IP and the clients repeatedly, huge communication cost is incurred at the Information Proprietor side. To address this challenge, we introduce Verifiable Auditing of Outsourced Database with Token Enforced Cloud Storage (VOTE) mechanism based on Merkle Hash Tree (MHT), Invertible Bloom Filter(IBF) and Counting Bloom Filter(CBF). The proposed scheme reduces the huge communication cost at the Information Proprietor side and achieves preciseness and intactness of the search outcome. Experimental analysis show that the proposed scheme has totally reduced the huge communication cost at the Information Proprietor side, and simultaneously achieves the preciseness and intactness of search outcome though the semi- trusted CSP deliberately sends a null set

scholarly journals Architectural Framework for Intelligent and Secure Aadhar Data Management Synchronized with both Offline and Online Transaction Systems

2020 ◽  
Vol 9 (3) ◽  
pp. 3105-3117
Keyword(s):  
Cloud Storage ◽  
Data Privacy ◽  
Storage Management ◽  
National Database ◽  
Service Provisioning ◽  
Unique Identity ◽  
Architectural Framework ◽  
Efficient Data ◽  
The Government ◽  
And Storage

The information of the citizens identity are reposited in the national database system that demands higher degree of security features in order to combat the privacy problems associated with it. The information retained within an identity of a citizen is highly valuable as well as sensitive as it is constructed by integrating various forms of biometric trails e.g. iris, fingerprint etc along with their personal details. Aadhar is one of such initiatives by Government of India by their “Unique Identification Authority of India (UIDAI) project” that generates 12-digit random number as unique identity. The Government of India is in progressive approach to integrate various social and security related applications to Aadhar in order to achieve identification-based service provisioning. In the recent news, Aadhar details have been reported to be encountered a serious security breach that endangered the private details of the Aadhar card holders. Therefore, a robust and efficient secure eco-system is required to keep national ID database secure. This chapter aims to describe novel framework for Aadhar data privacy and storage management by adopting efficient data blockage and transformation over cloud storage using statistical method based on data embedding by blocking and transformation.

Efficient Update Control of Bloom Filter Replicas in Large Scale Distributed Systems

2010 ◽  
pp. 785-807 ◽  
Author(s):  
Yifeng Zhu ◽  
Hong Jiang
Keyword(s):  
Distributed Systems ◽  
Large Scale ◽  
Control Mechanism ◽  
False Negative ◽  
Bloom Filter ◽  
Analytical Models ◽  
Bloom Filters ◽  
Distributed Environment ◽  
Membership Query ◽  
Efficient Data

This chapter discusses the false rates of Bloom filters in a distributed environment. A Bloom filter (BF) is a space-efficient data structure to support probabilistic membership query. In distributed systems, a Bloom filter is often used to summarize local services or objects and this Bloom filter is replicated to remote hosts. This allows remote hosts to perform fast membership query without contacting the original host. However, when the services or objects are changed, the remote Bloom replica may become stale. This chapter analyzes the impact of staleness on the false positive and false negative for membership queries on a Bloom filter replica. An efficient update control mechanism is then proposed based on the analytical results to minimize the updating overhead. This chapter validates the analytical models and the update control mechanism through simulation experiments.

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