A Cloud Privacy-Preserving Model Using Gaussian Mutation-Based Bacterial Foraging Optimization and Genetic Crossover Operation Article Swipe
Kartik Anand
,
A. Vijayaraj
,
M. Anand
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-354988/v1
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-354988/v1
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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-354988/v1
- https://www.researchsquare.com/article/rs-354988/latest.pdf
- OA Status
- green
- References
- 14
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3159951989
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3159951989Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-354988/v1Digital Object Identifier
- Title
-
A Cloud Privacy-Preserving Model Using Gaussian Mutation-Based Bacterial Foraging Optimization and Genetic Crossover OperationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-04-16Full publication date if available
- Authors
-
Kartik Anand, A. Vijayaraj, M. AnandList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-354988/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-354988/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-354988/latest.pdfDirect OA link when available
- Concepts
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Cloud computing, Computer science, Crossover, Key (lock), Flexibility (engineering), Information privacy, Genetic algorithm, Distributed computing, Outsourcing, Computer security, Artificial intelligence, Machine learning, Mathematics, Political science, Statistics, Operating system, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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14Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.rising | 20 |
| abstract_inverted_index.study, | 159 |
| abstract_inverted_index.system | 8 |
| abstract_inverted_index.tackle | 110 |
| abstract_inverted_index.(CGBFO- | 174 |
| abstract_inverted_index.CDNNCS, | 227 |
| abstract_inverted_index.Chaotic | 162 |
| abstract_inverted_index.access, | 42 |
| abstract_inverted_index.crucial | 45 |
| abstract_inverted_index.dealing | 67 |
| abstract_inverted_index.genetic | 171 |
| abstract_inverted_index.harvest | 18 |
| abstract_inverted_index.methods | 115 |
| abstract_inverted_index.offered | 135 |
| abstract_inverted_index.optimal | 79, 178, 197 |
| abstract_inverted_index.privacy | 80, 90, 97, 155 |
| abstract_inverted_index.private | 30 |
| abstract_inverted_index.results | 214 |
| abstract_inverted_index.studies | 83 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Deriving | 181 |
| abstract_inverted_index.Foraging | 168 |
| abstract_inverted_index.Gaussian | 165 |
| abstract_inverted_index.However, | 126 |
| abstract_inverted_index.advanced | 51 |
| abstract_inverted_index.agility, | 130 |
| abstract_inverted_index.existing | 74, 222 |
| abstract_inverted_index.function | 184 |
| abstract_inverted_index.hosting. | 141 |
| abstract_inverted_index.involved | 151 |
| abstract_inverted_index.personal | 21 |
| abstract_inverted_index.proposed | 154, 161, 206 |
| abstract_inverted_index.provides | 210 |
| abstract_inverted_index.repeated | 63 |
| abstract_inverted_index.replica. | 156 |
| abstract_inverted_index.required | 57 |
| abstract_inverted_index.savings, | 129 |
| abstract_inverted_index.security | 4, 48, 219 |
| abstract_inverted_index.Bacterial | 167 |
| abstract_inverted_index.SAS-DPSO, | 226 |
| abstract_inverted_index.algorithm | 176, 209 |
| abstract_inverted_index.cleansing | 143 |
| abstract_inverted_index.computing | 138 |
| abstract_inverted_index.crossover | 172 |
| abstract_inverted_index.different | 82 |
| abstract_inverted_index.efficient | 121, 212 |
| abstract_inverted_index.increases | 9 |
| abstract_inverted_index.necessity | 2 |
| abstract_inverted_index.operation | 173 |
| abstract_inverted_index.performed | 85 |
| abstract_inverted_index.resultant | 61 |
| abstract_inverted_index.sensitive | 23, 69 |
| abstract_inverted_index.algorithm. | 203 |
| abstract_inverted_index.artificial | 103 |
| abstract_inverted_index.businesses | 133 |
| abstract_inverted_index.chemotaxis | 163 |
| abstract_inverted_index.computing. | 125 |
| abstract_inverted_index.constraint | 55 |
| abstract_inverted_index.generation | 199 |
| abstract_inverted_index.outsourced | 39 |
| abstract_inverted_index.parameters | 185 |
| abstract_inverted_index.protection | 54 |
| abstract_inverted_index.strategic, | 118 |
| abstract_inverted_index.techniques | 75, 106 |
| abstract_inverted_index.volume.The | 25 |
| abstract_inverted_index.Ultimately, | 204 |
| abstract_inverted_index.challenges. | 112 |
| abstract_inverted_index.controllers | 17 |
| abstract_inverted_index.flexibility | 132 |
| abstract_inverted_index.generation. | 180 |
| abstract_inverted_index.performance | 213 |
| abstract_inverted_index.restoration | 145 |
| abstract_inverted_index.statements. | 49 |
| abstract_inverted_index.unprotected | 29 |
| abstract_inverted_index.violations. | 65 |
| abstract_inverted_index.Optimization | 169 |
| abstract_inverted_index.accomplishes | 196 |
| abstract_inverted_index.intelligence | 104 |
| abstract_inverted_index.modification | 193 |
| abstract_inverted_index.preservation | 98, 188 |
| abstract_inverted_index.organizations | 122 |
| abstract_inverted_index.preservation. | 91 |
| abstract_inverted_index.mutation-based | 166 |
| abstract_inverted_index.insight-driven, | 117 |
| abstract_inverted_index.multi-objective | 183 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.07030318 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |