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.

Optimal Key Generation for Data Sanitization and Restoration of Cloud Data: Future of Financial Cyber Security

2020 ◽  
Vol 19 (04) ◽  
pp. 987-1013
Author(s):  
B. Balashunmugaraja ◽  
T. R. Ganeshbabu
Keyword(s):  
Cyber Security ◽  
Privacy Preservation ◽  
Service Sector ◽  
Scale Up ◽  
Cloud Security ◽  
Sensitive Information ◽  
Key Generation ◽  
Sensitive Data ◽  
Data Sanitization ◽  
Data Restoration

Cloud security in finance is considered as the key importance, taking account of the aspect of critical data stored over cloud spaces within organizations all around the globe. They are chiefly relying on cloud computing to accelerate their business profitability and scale up their business processes with enhanced productivity coming through flexible work environments offered in cloud-run working systems. Hence, there is a prerequisite to contemplate cloud security in the entire financial service sector. Moreover, the main issue challenged by privacy and security is the presence of diverse chances to attack the sensitive data by cloud operators, which leads to double the user’s anxiety on the stored data. For solving this problem, the main intent of this paper is to develop an intelligent privacy preservation approach for data stored in the cloud sector, mainly the financial data. The proposed privacy preservation model involves two main phases: (a) data sanitization and (b) data restoration. In the sanitization process, the sensitive data is hidden, which prevents sensitive information from leaking on the cloud side. Further, the normal as well as the sensitive data is stored in a cloud environment. For the sanitization process, a key should be generated that depends on the new meta-heuristic algorithm called crossover improved-lion algorithm (CI-LA), which is inspired by the lion’s unique social behavior. During data restoration, the same key should be used for effectively restoring the original data. Here, the optimal key generation is done in such a way that the objective model involves the degree of modification, hiding rate, and information preservation rate, which effectively enhance the cyber security performance in the cloud.

scholarly journals eHealth Cloud Security Challenges: A Survey

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Yazan Al-Issa ◽  
Mohammad Ashraf Ottom ◽  
Ahmed Tamrawi
Keyword(s):  
Cloud Computing ◽  
Resource Sharing ◽  
Energy Savings ◽  
Service Providers ◽  
Healthcare Providers ◽  
Cloud Service ◽  
Cloud Security ◽  
Security And Privacy ◽  
Healthcare Industry ◽  
Sensitive Data

Cloud computing is a promising technology that is expected to transform the healthcare industry. Cloud computing has many benefits like flexibility, cost and energy savings, resource sharing, and fast deployment. In this paper, we study the use of cloud computing in the healthcare industry and different cloud security and privacy challenges. The centralization of data on the cloud raises many security and privacy concerns for individuals and healthcare providers. This centralization of data (1) provides attackers with one-stop honey-pot to steal data and intercept data in-motion and (2) moves data ownership to the cloud service providers; therefore, the individuals and healthcare providers lose control over sensitive data. As a result, security, privacy, efficiency, and scalability concerns are hindering the wide adoption of the cloud technology. In this work, we found that the state-of-the art solutions address only a subset of those concerns. Thus, there is an immediate need for a holistic solution that balances all the contradicting requirements.

Privacy preserving model-based authentication and data security in cloud computing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ankush Balaram Pawar ◽  
Dr. Shashikant U. Ghumbre ◽  
Dr. Rashmi M. Jogdand
Keyword(s):  
Cloud Computing ◽  
Data Sharing ◽  
Data Security ◽  
Privacy Preservation ◽  
Advanced Technology ◽  
Key Generation ◽  
Content Type ◽  
Preservation Method ◽  
Data Owner ◽  
Setup Phase

Purpose Cloud computing plays a significant role in the initialization of secure communication between users. The advanced technology directs to offer several services, such as platform, resources, and accessing the network. Furthermore, cloud computing is a broader technology of communication convergence. In cloud computing architecture, data security and authentication are the main significant concerns. Design/methodology/approach The purpose of this study is to design and develop authentication and data security model in cloud computing. This method includes six various units, such as cloud server, data owner, cloud user, inspection authority, attribute authority, and central certified authority. The developed privacy preservation method includes several stages, namely setup phase, key generation phase, authentication phase and data sharing phase. Initially, the setup phase is performed through the owner, where the input is security attributes, whereas the system master key and the public parameter are produced in the key generation stage. After that, the authentication process is performed to identify the security controls of the information system. Finally, the data is decrypted in the data sharing phase for sharing data and for achieving data privacy for confidential data. Additionally, dynamic splicing is utilized, and the security functions, such as hashing, Elliptic Curve Cryptography (ECC), Data Encryption Standard-3 (3DES), interpolation, polynomial kernel, and XOR are employed for providing security to sensitive data. Findings The effectiveness of the developed privacy preservation method is estimated based on other approaches and displayed efficient outcomes with better privacy factor and detection rate of 0.83 and 0.65, and time is highly reduced by 2815ms using the Cleveland dataset. Originality/value This paper presents the privacy preservation technique for initiating authenticated encrypted access in clouds, which is designed for mutual authentication of requester and data owner in the system.

scholarly journals A Lightweight Proxy Re-Encryption Approach with Certificate-Based and Incremental Cryptography for Fog-Enabled E-Healthcare

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Junaid Hassan ◽  
Danish Shehzad ◽  
Insaf Ullah ◽  
Fahad Algarni ◽  
Muhammad Umar Aftab ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Fog Computing ◽  
Bilinear Pairing ◽  
Response Monitoring ◽  
Key Generation ◽  
Distributed Environment ◽  
Sensitive Data ◽  
User Data ◽  
The Impact

Cloud computing aims to provide reliable, customized, and quality of service (QoS) guaranteed dynamic computing environments for end-users. However, there are applications such as e-health and emergency response monitoring that require quick response and low latency. Delays caused by transferring data over the cloud can seriously affect the performance and reliability of real-time applications. Before outsourcing e-health care data to the cloud, the user needs to perform encryption on these sensitive data to ensure its confidentiality. Conventionally, any modification to the user data requires encrypting the entire data and calculating the hash of the data from scratch. This data modification mechanism increases communication and computation costs over the cloud. The distributed environment of fog computing is used to overcome the limitations of cloud computing. This paper proposed a certificate-based incremental proxy re-encryption scheme (CB-PReS) for e-health data sharing in fog computing. The proposed scheme improves the file modification operations, i.e., updation, deletion, and insertion. The proposed scheme is tested on the iFogSim simulator. The iFogSim simulator facilitates the development of models for fog and IoT environments, and it also measures the impact of resource management techniques regarding network congestion and latency. Experiments depict that the proposed scheme is better than the existing schemes based on expensive bilinear pairing and elliptic curve techniques. The proposed scheme shows significant improvement in key generation and file modification time.

scholarly journals AN ENHANCED PRIVACY PRESERVING APPROACH WITH ENFORCING POLICIES FOR PROCESSING BIG DATA IN SPARK FRAMEWORK

2018 ◽  
Vol 7 (3.10) ◽  
Author(s):  
Revathy Swaminathan ◽  
Arunkumar Thangavelu
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Privacy Preservation ◽  
Personal Information ◽  
Key Generation ◽  
Security Measures ◽  
Sensitive Data ◽  
Time Consumption ◽  
Critical Process ◽  
Spark Framework

Ensuring the privacy for the big data stored in a cloud system is one of the demanding and critical process in recent days. Generally, the big data contains a huge amount of data, which requires some security measures and rules for assuring the confidentiality.  For this reason, different techniques have been developed in the traditional works, which intends to guarantee the privacy of the big data by implementing key generation, encryption, and anonymization mechanisms. But, it limits the issues of increased time consumption, computational complexity, and error rate. Thus, the proposed work aims to design an enhanced mechanism for a secure big data storage. Here, the user’s bank dataset is considered as the input, which is protected from the unauthorized users by guaranteeing both the privacy and secrecy of the data. Here, the raw dataset is preprocessed to increase the data quality and correctness. Then, the security policies (i.e. rules) are generated for allowing the restricted access on the data by using an Improved FP-Growth (IFP-G) algorithm. Consequently, the sensitive and non-sensitive data attributes are classified based on the extracted features by using an Enhanced Random Forest (ERF) classification technique. At last, the privacy of user’s personal information and other details are protected with the use of a Modified Incognito Anonymization based Privacy Preservation (MIA-PP) algorithm. These enhanced mechanisms guarantee the security and confidentiality of the big data with reduced time consumption and increased accuracy. During experimental evaluation, the results of the proposed privacy mechanism is analyzed and compared by using different measures. Also, some of the existing anonymization and classification techniques have been considered to prove the betterment of the proposed technique. 

scholarly journals Route Map of Privacy Preservation to IOT

2018 ◽  
Vol 7 (2.7) ◽  
pp. 825
Author(s):  
Mr Pravin N. Kathavate ◽  
Dr J. Amudhavel
Keyword(s):  
Data Hiding ◽  
Privacy Preservation ◽  
Key Generation ◽  
Performance Study ◽  
Sensitive Data ◽  
Research Papers ◽  
Research Issues ◽  
Data Anonymization ◽  
Data Preservation ◽  
Data Restoration

Data anonymization is the main feature of privacy preservation, and it assists in eradicating the privacy hazard in data preparation in various applications including IoT. Pseudonymity and Anonymization are two significant security factors that were adopted when sensitive data are shared.  In medical field, data is usually distributed horizontally with diverse regions carrying a similar set of characteristics for various anonymization techniques. Accordingly, this paper intends to formulate a review on privacy preservation in IoT. Here, the literature analyses on diverse techniques associated with data hiding, data preservation and data anonymization along with data restoration properties. It reviews 60 research papers and states the significant analysis. Initially, the analysis depicts the chronological review of the overall contribution of different types of anonymization protocols in diverse applications. Subsequently, the analysis also focuses on various features such as applications, measures, key generation and data preservation in healthcare, etc. Furthermore, this paper provides the detailed performance study regarding data hiding and restoration process in each contribution. Finally, it extends the various research issues which can be useful for the researchers to accomplish further research on data preservation in IoT. 

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