Multi-keyword Ranked Search with Fine-grained Access Control over Encrypted Cloud Data

The Data Base as a Service is a great example where the database engine and storage devices are in cloud data. This scheme allows customers to outsource data and store in cloud database on pay per user, scalable and flexible. But data confidentiality is in high risk when data is outsourced and stored in third party database. A trusted third party server must be maintaining the third party data base. There is a possibility of malicious administrator who can leaks the data which is stored in third party database. The best method is to encrypt the data and store in third party database but alone encryption is not sufficient. Even authorization is another problem that who can access the data. For data security and authorized of users, the fine grained access control policy Cipher text policy Attribute Based encryption (CP-ABE) is used to give access to authorized users only and the best symmetric encryption Advanced Encryption Standard(AES) is applied on data before outsourcing the data in cloud.

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Ensuring Security, Confidentiality and Fine-Grained Data Access Control of Cloud Data Storage Implementation Environment

Journal of Information Security ◽

10.4236/jis.2015.62013 ◽

2015 ◽

Vol 06 (02) ◽

pp. 118-130 ◽

Cited By ~ 1

Author(s):

Amir Mohamed Talib

Keyword(s):

Access Control ◽

Data Storage ◽

Data Access ◽

Cloud Data ◽

Fine Grained ◽

Data Access Control ◽

Cloud Data Storage

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Data Security for Cloud Datasets With Bloom Filters on Ciphertext Policy Attribute Based Encryption

Research Anthology on Artificial Intelligence Applications in Security ◽

10.4018/978-1-7998-7705-9.ch064 ◽

2021 ◽

pp. 1431-1447

Author(s):

G. Sravan Kumar ◽

A. Sri Krishna

Keyword(s):

Access Control ◽

Data Security ◽

Bloom Filters ◽

Communication Overhead ◽

Cloud Data ◽

Access Policy ◽

Fine Grained ◽

Attribute Based Encryption ◽

Cloud Data Storage ◽

Ciphertext Policy

Cloud data storage environments allow the data providers to store and share large amounts of datasets generated from various resources. However, outsourcing private data to a cloud server is insecure without an efficient access control strategy. Thus, it is important to protect the data and privacy of user with a fine-grained access control policy. In this article, a Bloom Filter-based Ciphertext-Policy Attribute-Based Encryption (BF-CP-ABE) technique is presented to provide data security to cloud datasets with a Linear Secret Sharing Structure (LSSS) access policy. This fine-grained access control scheme hides the whole attribute set in the ciphertext, whereas in previous CP-ABE methods, the attributes are partially hidden in the ciphertext which in turn leaks private information about the user. Since the attribute set of the BF-CP-ABE technique is hidden, bloom filters are used to identify the authorized users during data decryption. The BF-CP-ABE technique is designed to be selective secure under an Indistinguishable-Chosen Plaintext attack and the simulation results show that the communication overhead is significantly reduced with the adopted LSSS access policy.

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Achieving Fine-Grained Access Control with Discretionary User Revocation over Cloud Data

2018 IEEE Conference on Communications and Network Security (CNS) ◽

10.1109/cns.2018.8433128 ◽

2018 ◽

Cited By ~ 1

Author(s):

Qiuxiang Dong ◽

Dijiang Huang ◽

Jim Luo ◽

Myong Kang

Keyword(s):

Access Control ◽

Cloud Data ◽

Fine Grained ◽

User Revocation

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Practical Multi-Keyword Ranked Search with Time- Based Access Control over Encrypted Cloud Data

International Journal of Advanced Research in Science, Communication and Technology ◽

10.48175/ijarsct-1212 ◽

2021 ◽

pp. 72-78

Keyword(s):

Access Control ◽

Time Window ◽

Security Analysis ◽

Third Party ◽

Security And Privacy ◽

Sensitive Data ◽

K Nearest Neighbors ◽

Cloud Data ◽

Massive Growth ◽

Ranked Search

The recent trends suggest that there is an increase in the inclination towards storing data in the cloud due to explosive and massive growth of the volume of the data in the cloud computing environment. It helps them to reduce their computational and storage costs but also undeniably brought in concerns about security and privacy as the owners of the highly sensitive data lose control of it directly. The sensitive data could include electronic-based medical records, confidential fiscal documents, etc. An increased distrust about storage of files in a third-party service provider of cloud resources would contradict the very same reason for which cloud storage facilities were introduced. That’s because we cannot deny the fact that cloud based storage systems offer on- demand and ubiquitous access to flexible storage and computational resources. The keyword ranked search methodologies used in the existing systems mainly focus on enhancing and enriching the efficiency of searching the files and their respective functionalities but a lack of straight forward analysis of security and issues with providing access control have not been addressed. To address these disadvantages, in this paper, we propose an efficient Multi-Keyword Ranked Search scheme with Fine-grained access control (MRSF).MRSF is a methodology which can combine matching of coordinates technique with Term Frequency-Inverse Document Frequency (TF-IDF) to thereby achieve a highly precise retrieval of any cipher text of interest. It also improves the secure k-nearest neighbors (kNN) method. By utilizing an access strategy which is polynomial based, it can effectively refine the search privileges of the users’. Professional security analysis proves that MRSF is secure with respect to safeguarding the secrecy of outsourced data and the privacy of tokens and indices. Along with this enhanced methodology of ranked search scheme, a time limit based access control feature has also been proposed to ensure that the adaptive attackers are stalled from giving prolonged access to the data files. Session expiry will ensure security of data and that is to be achieved by providing a time window for the file retrieval. Extensive experiments also show that MRSF reaches higher search precision and many more functionalities when compared to the existing systems.

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