Exponential Ant-Lion Rider Optimization for Privacy Preservation in Cloud Computing

2021 ◽  
pp. 1-19
Author(s):  
Nagaraju Pamarthi ◽  
N. Nagamalleswara Rao
Keyword(s):  
Cloud Computing ◽  
Optimization Algorithm ◽  
Privacy Preservation ◽  
Fitness Function ◽  
Information Loss ◽  
Superior Performance ◽  
Data Matrix ◽  
Sensitive Information ◽  
Bilinear Map ◽  
Ant Lion

The innovative trend of cloud computing is outsourcing data to the cloud servers by individuals or enterprises. Recently, various techniques are devised for facilitating privacy protection on untrusted cloud platforms. However, the classical privacy-preserving techniques failed to prevent leakage and cause huge information loss. This paper devises a novel methodology, namely the Exponential-Ant-lion Rider optimization algorithm based bilinear map coefficient Generation (Exponential-AROA based BMCG) method for privacy preservation in cloud infrastructure. The proposed Exponential-AROA is devised by integrating Exponential weighted moving average (EWMA), Ant Lion optimizer (ALO), and Rider optimization algorithm (ROA). The input data is fed to the privacy preservation process wherein the data matrix, and bilinear map coefficient Generation (BMCG) coefficient are multiplied through Hilbert space-based tensor product. Here, the bilinear map coefficient is obtained by multiplying the original data matrix and with modified elliptical curve cryptography (MECC) encryption to maintain data security. The bilinear map coefficient is used to handle both the utility and the sensitive information. Hence, an optimization-driven algorithm is utilized to evaluate the optimal bilinear map coefficient. Here, the fitness function is newly devised considering privacy and utility. The proposed Exponential-AROA based BMCG provided superior performance with maximal accuracy of 94.024%, maximal fitness of 1, and minimal Information loss of 5.977%.

A Novel Approach For Privacy Preservation In Blockchain Network Using Tensor Product And A Hybrid Swarm Intelligence

2021 ◽  
Vol 12 (4) ◽  
pp. 0-0
Keyword(s):  
Tensor Product ◽  
Swarm Intelligence ◽  
Privacy Preservation ◽  
Information Loss ◽  
Data Matrix ◽  
Sensitive Information ◽  
Hybrid Swarm ◽  
Novel Approach ◽  
Block Chain ◽  
Minimal Information

Therefore, block chain-based technique is developed for privacy protection using tensor product and a hybrid swarm intelligence based coefficient generation. Initially, the block chain data with mixed attributes was subjected to the privacy preservation process, in which the raw data matrix and solitude and utility (SU) coefficient were multiplied through the tensor product. Thus, the derivation of the SU coefficient, which handles both sensitive information and utility, was formulated as a searching problem. Then, the proposed algorithm was introduced to evaluate the SU coefficient. The performance of the developed technique was evaluated by means of accuracy and information loss. The achieved results have shown that the developed hybrid sward intelligence reached a maximal accuracy of 0.840 and minimal information loss of 0.159 using dataset-2, compared to the existing system.

Adaptive Privacy Preservation Approach for Big Data Publishing in Cloud using K-Anonymization

2020 ◽  
Vol 13 ◽  
Author(s):  
Suman Madan ◽  
Puneet Goswami
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Particle Swarm Optimization ◽  
Classification Accuracy ◽  
Privacy Preservation ◽  
Fitness Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Information Loss ◽  
Swarm Optimization

Background: Big data is an emerging technology that has numerous applications in the fields, like hospitals, government records, social sites, and so on. As the cloud computing can transfer large amount of data through servers it has found its importance in big data. Hence, it is important in cloud computing to protect the data so that the third party users cannot access the information from the users. Methods: This paper develops an anonymization model and adaptive Dragon Particle Swarm Optimization (adaptive Dragon-PSO) algorithm for privacy preservation in cloud environment. The development of proposed adaptive DragonPSO incorporates the integration of adaptive idea in the dragon-PSO algorithm. The dragon-PSO is the integration of Dragonfly Algorithm (DA) and Particle Swarm Optimization (PSO) algorithm. The proposed method derives the fitness function for the proposed adaptive Dragon-PSO algorithm to attain the higher value of privacy and utility. The performance of the proposed method was evaluated using the metrics, such as information loss and classification accuracy for different anonymization constant values. Conclusion: The proposed method provided a minimal information loss and maximal classification accuracy of 0.0110 and 0.7415, respectively when compared with the existing methods.

scholarly journals A Bacterial Chemotaxis-Inspired Coordination Strategy for Coverage and Aggregation of Swarm Robots

2021 ◽  
Vol 11 (3) ◽  
pp. 1347
Author(s):  
Laihao Jiang ◽  
Hongwei Mo ◽  
Peng Tian
Keyword(s):  
Optimization Algorithm ◽  
Fitness Function ◽  
Bacterial Chemotaxis ◽  
Superior Performance ◽  
Bacterial Foraging Optimization ◽  
Random Factor ◽  
Bacterial Colony ◽  
Coordination Strategies ◽  
Coordination Strategy ◽  
Swarm Robots

Many bio-inspired coordination strategies have been investigated for swarm robots. Bacterial chemotaxis exhibits a certain degree of intelligence, and has been developed some optimization algorithm for robot(s), e.g., bacterial foraging optimization algorithm (BFOA) and bacterial colony chemotaxis optimization algorithm (BCC). This paper proposes a bacterial chemotaxis-inspired coordination strategy (BCCS) of swarm robotic systems for coverage and aggregation. The coverage is the problem of finding a solution to uniformly deploy robots on a given bounded space. To solve this problem, this paper uses chaotic preprocessing to generate the initial positions of the robots. After the initialization, each robot calculates the area only covered by itself as the fitness function value. Then, each robot makes an action, running or rotating, depending on coordination strategy inspired bacterial chemotaxis. Moreover, we extend this solution and introduce a random factor to overcome aggregation, which is to guide robots to rendezvous at an unspecified point. The simulation results demonstrate the superior performance of the proposed coordination strategy in both success rate and an average number of iterations than other controllers.

scholarly journals Data Privacy Preservation using AES-GCM Encryption in Heroku Cloud

2019 ◽  
Vol 8 (3) ◽  
pp. 7544-7548
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Service Industry ◽  
Computational Cost ◽  
Cloud Service ◽  
Third Party ◽  
Sensitive Information ◽  
Cloud Data

The increasing popularity of cloud data storage and its ever-rising versatility, shows that cloud computing is one of the most widely excepted phenomena. It not only helps with powerful computing facilities but also reduce a huge amount of computational cost. And with such high demand for storage has raised the growth of the cloud service industry that provides an affordable, easy-to-use and remotely-accessible services. But like every other emerging technology it carries some inherent security risks associated and cloud storage is no exception. The prime reason behind it is that users have to blindly trust the third parties while storing the useful information, which may not work in the best of interest. Hence, to ensure the privacy of sensitive information is primarily important for any public, third-party cloud. In this paper, we mainly focus on proposing a secure cloud framework with encrypting sensitive data’s using AES-GCM cryptographic techniques in HEROKU cloud platform. Here we tried to implement Heroku as a cloud computing platform, used the AES-GCM algorithm and evaluate the performance of the said algorithm. Moreover, analyses the performance of AES/GCM execution time with respect to given inputs of data

scholarly journals Enhancement of k-anonymity algorithm for privacy preservation in social media

2018 ◽  
Vol 7 (2.18) ◽  
pp. 40
Author(s):  
Aanchal Sharma ◽  
Sudhir Pathak
Keyword(s):  
Social Network ◽  
Privacy Preservation ◽  
Research Work ◽  
Information Loss ◽  
Data Publishing ◽  
Similar Degree ◽  
Sensitive Information ◽  
Social Network Data ◽  
Traditional Research ◽  
The Social

In recent times, more and more social data is transmitted in different ways. Protecting the privacy of social network data has turn out to be an essential issue. Hypothetically, it is assumed that the attacker utilizes the similar information used by the genuine user. With the knowledge obtained from the users of social networks, attackers can easily attack the privacy of several victims. Thus, assuming the attacks or noise node with the similar environment information does not resemble the personalized privacy necessities, meanwhile, it loses the possibility to attain better utility by taking benefit of differences of users’ privacy necessities. The traditional research on privacy-protected data publishing can only deal with relational data and even cannot applied to the data of social networking. In this research work, K-anonymity is used for providing the security of the sensitive information from the attacker in the social network. K-anonymity provides security from attacker by making the graph and developing nodes degree. The clusters are made by grouping the similar degree into one group and the process is repeated until the noisy node is identified. For measuring the efficiency the parameters named as Average Path Length (APL) and information loss are measured. A reduction of 0.43% of the information loss is obtained.  

scholarly journals A New “Good and Bad Groups-Based Optimizer” for Solving Various Optimization Problems

2021 ◽  
Vol 11 (10) ◽  
pp. 4382
Author(s):  
Ali Sadeghi ◽  
Sajjad Amiri Doumari ◽  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Pavel Trojovský ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Gray Wolf ◽  
Good Group ◽  
Good Ability ◽  
Whale Optimization

Optimization is the science that presents a solution among the available solutions considering an optimization problem’s limitations. Optimization algorithms have been introduced as efficient tools for solving optimization problems. These algorithms are designed based on various natural phenomena, behavior, the lifestyle of living beings, physical laws, rules of games, etc. In this paper, a new optimization algorithm called the good and bad groups-based optimizer (GBGBO) is introduced to solve various optimization problems. In GBGBO, population members update under the influence of two groups named the good group and the bad group. The good group consists of a certain number of the population members with better fitness function than other members and the bad group consists of a number of the population members with worse fitness function than other members of the population. GBGBO is mathematically modeled and its performance in solving optimization problems was tested on a set of twenty-three different objective functions. In addition, for further analysis, the results obtained from the proposed algorithm were compared with eight optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), teaching–learning-based optimization (TLBO), gray wolf optimizer (GWO), and the whale optimization algorithm (WOA), tunicate swarm algorithm (TSA), and marine predators algorithm (MPA). The results show that the proposed GBGBO algorithm has a good ability to solve various optimization problems and is more competitive than other similar algorithms.

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