Privacy Preservation in Cloud Computing Using Randomized Encoding

2021 ◽  
Author(s):  
Parmod Kalia ◽  
Divya Bansal ◽  
Sanjeev Sofat
Keyword(s):  
Cloud Computing ◽  
Privacy Preservation ◽  
Randomized Encoding

A Cloud Privacy-Preserving Model Using Gaussian Mutation-Based Bacterial Foraging Optimization and Genetic Crossover Operation

2021 ◽  
Author(s):  
K Anand ◽  
A. Vijayaraj ◽  
M. Vijay Anand
Keyword(s):  
Cloud Computing ◽  
Privacy Preservation ◽  
Cost Savings ◽  
Cloud Security ◽  
Public Access ◽  
Key Generation ◽  
Gaussian Mutation ◽  
Bacterial Foraging Optimization ◽  
Sensitive Data ◽  
Bacterial Foraging

Abstract The necessity of security in the cloud system increases day by day in which the data controllers harvest the rising personal and sensitive data volume.The cloud has some unprotected private data as well as data that has been outsourced for public access, which is crucial for cloud security statements. An advanced legal data protection constraint is required due to the resultant of repeated data violations. While dealing with sensitive data, most of the existing techniques failed to handle optimal privacy and different studies were performed to take on cloud privacy preservation. Hence, the novel model of privacy preservation in the cloud and artificial intelligence (AI) techniques were used to tackle these challenges. These AI methods are insight-driven, strategic, and more efficient organizations in cloud computing. However, the cost savings, agility, higher flexibility businesses are offered with cloud computing by data hosting. Data cleansing and restoration are the two major steps involved in the proposed privacy replica. In this study, we proposed Chaotic chemotaxis and Gaussian mutation-based Bacterial Foraging Optimization with genetic crossover operation (CGBFO- GC) algorithm for optimal key generation. Deriving the multi-objective function parameters namely data preservation ratio, hiding ratio, and modification degree that accomplishes optimal key generation using CGBFO- GC algorithm. Ultimately, the proposed CGBFO- GC algorithm provides more efficient performance results in terms of cloud security than an existing method such as SAS-DPSO, CDNNCS, J-SSO, and GC.

A Framework for Protecting Users' Privacy in Cloud

2019 ◽  
pp. 378-389
Author(s):  
Adesina S. Sodiya ◽  
Adegbuyi B.
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Privacy Preservation ◽  
Personal Data ◽  
Decomposition Algorithm ◽  
Privacy Concerns ◽  
Personally Identifiable Information ◽  
Foreign Key ◽  
Huge Challenge

Data and document privacy concerns are increasingly important in the online world. In Cloud Computing, the story is the same, as the secure processing of personal data represents a huge challenge. The main focus is to preserve and protect personally identifiable information (PII) of individuals, customers, businesses, governments and organisations. The current use of anonymization techniques is not quite efficient because of its failure to use the structure of the datasets under consideration and inability to use a metric that balances the usefulness of information with privacy preservation. In this work, an adaptive lossy decomposition algorithm was developed for preserving privacy in cloud computing. The algorithm uses the foreign key associations to determine the generalizations possible for any attribute in the database. It generates penalties for each obscured attribute when sharing and proposes an optimal decomposition of the relation. Postgraduate database of Federal University of Agriculture, Abeokuta, Nigeria and Adult database provided at the UCIrvine Machine Learning Repository were used for the evaluation. The result shows a system that could be used to improve privacy in cloud computing.

Privacy Preservation Based on Separation Sensitive Attributes for Cloud Computing

2021 ◽  
pp. 840-856
Author(s):  
Feng Xu ◽  
Mingming Su ◽  
Yating Hou
Keyword(s):  
Cloud Computing ◽  
Privacy Preservation ◽  
Personal Data ◽  
The Internet ◽  
Malicious Nodes ◽  
Privacy Leakage ◽  
Computing Paradigm ◽  
Data Release ◽  
Security Levels ◽  
Simulation Results

The Cloud computing paradigm can improve the efficiency of distributed computing by sharing resources and data over the Internet. However, the security levels of nodes (or severs) are not the same, thus, sensitive tasks and personal data may be scheduled (or shared) to some unsafe nodes, which can lead to privacy leakage. Traditional privacy preservation technologies focus on the protection of data release and process of communication, but lack protection against disposing sensitive tasks to untrusted computing nodes. Therefore, this article put forwards a protocol based on task-transformation, by which tasks will be transformed into another form in the task manager before they can be scheduled to other nodes. The article describes a privacy preservation algorithm based on separation sensitive attributes from values (SSAV) to realize the task-transformation function. This algorithm separates sensitive attributes in the tasks from their values, which make the malicious nodes cannot comprehend the real meaning of the values even they get the transformed tasks. Analysis and simulation results show that the authors' algorithm is more effective.

Adaptive multilevel fuzzy-based authentication framework to mitigate Cache side channel attack in cloud computing

2018 ◽  
Vol 09 (05) ◽  
pp. 1850045 ◽  
Author(s):  
Bharati Ainapure ◽  
Deven Shah ◽  
A. Ananda Rao
Keyword(s):  
Cloud Computing ◽  
Private Information ◽  
Privacy Preservation ◽  
Virtual Machines ◽  
Fuzzy Rule ◽  
Data Access ◽  
Side Channel ◽  
Side Channel Attack ◽  
Side Channel Attacks ◽  
Cloud Providers

Cloud computing supports multitenancy to satisfy the users’ demands for accessing resources and simultaneously it increases revenue for cloud providers. Cloud providers adapt multitenancy by virtualizing the resources, like CPU, network interfaces, peripherals, hard drives and memory using hypervisor to fulfill the demand. In a virtualized environment, many virtual machines (VMs) can run on the same core with the help of the hypervisor by sharing the resources. The VMs running on the same core are the target for the malicious or abnormal attacks like side channel attacks. Among various side channel attacks in cloud computing, cache-based side channel attack is one that leaks private information of the users based on the shared resources. Here, as the shared resource is the cache, a process can utilize the cache usage of another by cache contention. Cache sharing provides a way for the attackers to gain considerable information so that the key used for encryption can be inferred. Discovering this side channel attack is a challenging task. This requires identification of a feature that influences the attack. Even though there are various techniques available in the literature to mitigate such attacks, an effective solution to reduce the cache-based side channel attack is still an issue. Therefore, a novel fuzzy rule-based mechanism is integrated to detect the cache side channel attackers by monitoring the cache data access (CDA). The factor that determines the attack is CDA in a log file created by the framework during authorization. The proposed framework also utilizes certain security properties including ECC and hashing for the privacy preservation and the decision is made with the aid of a fuzzy logic system.

A Framework for Protecting Users' Privacy in Cloud

2016 ◽  
Vol 10 (4) ◽  
pp. 33-43 ◽  
Author(s):  
Adesina S. Sodiya ◽  
Adegbuyi B.
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Privacy Preservation ◽  
Personal Data ◽  
Decomposition Algorithm ◽  
Privacy Concerns ◽  
Personally Identifiable Information ◽  
Foreign Key ◽  
Huge Challenge

Data and document privacy concerns are increasingly important in the online world. In Cloud Computing, the story is the same, as the secure processing of personal data represents a huge challenge The main focus is to to preserve and protect personally identifiable information (PII) of individuals, customers, businesses, governments and organisations. The current use of anonymization techniques is not quite efficient because of its failure to use the structure of the datasets under consideration and inability to use a metric that balances the usefulness of information with privacy preservation. In this work, an adaptive lossy decomposition algorithm was developed for preserving privacy in cloud computing. The algorithm uses the foreign key associations to determine the generalizations possible for any attribute in the database. It generates penalties for each obscured attribute when sharing and proposes an optimal decomposition of the relation. Postgraduate database of Federal University of Agriculture, Abeokuta, Nigeria and Adult database provided at the UCIrvine Machine Learning Repository were used for the evaluation. The result shows a system that could be used to improve privacy in cloud computing.

A Framework for Protecting Users' Privacy in Cloud

2019 ◽  
pp. 479-490
Author(s):  
Adesina S. Sodiya ◽  
Adegbuyi B.
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Privacy Preservation ◽  
Personal Data ◽  
Decomposition Algorithm ◽  
Privacy Concerns ◽  
Personally Identifiable Information ◽  
Foreign Key ◽  
Huge Challenge

Data and document privacy concerns are increasingly important in the online world. In Cloud Computing, the story is the same, as the secure processing of personal data represents a huge challenge. The main focus is to preserve and protect personally identifiable information (PII) of individuals, customers, businesses, governments and organisations. The current use of anonymization techniques is not quite efficient because of its failure to use the structure of the datasets under consideration and inability to use a metric that balances the usefulness of information with privacy preservation. In this work, an adaptive lossy decomposition algorithm was developed for preserving privacy in cloud computing. The algorithm uses the foreign key associations to determine the generalizations possible for any attribute in the database. It generates penalties for each obscured attribute when sharing and proposes an optimal decomposition of the relation. Postgraduate database of Federal University of Agriculture, Abeokuta, Nigeria and Adult database provided at the UCIrvine Machine Learning Repository were used for the evaluation. The result shows a system that could be used to improve privacy in cloud computing.

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