Privacy preservation of data using modified rider optimization algorithm: Optimal data sanitization and restoration model

2021 ◽  
Author(s):  
Mohana Shivashankar ◽  
Sahaaya Arul Mary
Keyword(s):  
Optimization Algorithm ◽  
Privacy Preservation ◽  
Data Sanitization ◽  
Restoration Model

Self-Adaptive Optimization for Improved Data Sanitization and Restoration

2020 ◽  
Vol 28 (03) ◽  
pp. 391-420
Author(s):  
Geeta S. Navale ◽  
Suresh N. Mali
Keyword(s):  
Data Mining ◽  
Privacy Preservation ◽  
Loss Rate ◽  
Differential Evolution Algorithm ◽  
Adaptive Optimization ◽  
Data Set ◽  
Data Sanitization ◽  
Data Restoration ◽  
Evolution Algorithm ◽  
Self Adaptive

Nowadays, Data Sanitization is considered as a highly demanded area for solving the issue of privacy preservation in Data mining. Data Sanitization, means that the sensitive rules given by the users with the specific modifications and then releases the modified database so that, the unauthorized users cannot access the sensitive rules. Promisingly, the confidentiality of data is ensured against the data mining methods. The ultimate goal of this paper is to build an effective sanitization algorithm for hiding the sensitive rules given by users/experts. Meanwhile, this paper concentrates on minimizing the four sanitization research challenges namely, rate of hiding failure, rate of Information loss, rate of false rule generation and degree of modification. Moreover, this paper proposes a heuristic optimization algorithm named Self-Adaptive Firefly (SAFF) algorithm to generate the small length key for data sanitization and also to adopt lossless data sanitization and restoration. The generated optimized key is used for both data sanitation as well as the data restoration process. The proposed SAFF-based algorithm is compared and examined against the other existing sanitizing algorithms like Fire Fly (FF), Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution algorithm (DE) algorithms and the results have shown the excellent performance of proposed algorithm. The proposed algorithm is implemented in JAVA. The data set used are Chess, Retail, T10, and T40.

Lossless and robust privacy preservation of association rules in data sanitization

2018 ◽  
Vol 22 (S1) ◽  
pp. 1415-1428 ◽  
Author(s):  
Geeta S. Navale ◽  
Suresh N. Mali
Keyword(s):  
Association Rules ◽  
Privacy Preservation ◽  
Data Sanitization

scholarly journals Algorithmic Analysis on Adaptive Dragonfly based Optimal Key Generation for Privacy Preservation in IoT

2020 ◽  
Vol 9 (4) ◽  
pp. 915-922
Keyword(s):  
Privacy Preservation ◽  
The Internet ◽  
Key Generation ◽  
Dragonfly Algorithm ◽  
Data Sanitization ◽  
Algorithmic Analysis ◽  
Medical Healthcare ◽  
The Internet Of Things ◽  
Exchange Data ◽  
Selection Of

The Internet of Things (IoT) is a network of Internet-enabled devices that can sense, communicate, and react to changes in their environment. It is commonly applied in many applications, like building automation, medical healthcare systems, transportation, environment monitoring, and energy management. Billions of these computing devices are connected to the Internet to exchange data between themselves and/or their infrastructure. However, the privacy of data seems to be the greatest issue that needs to be solved. This paper intends to develop an improved data sanitization and restoration framework in IoT for higher-order privacy preservation. The preservation process is carried out using key that is optimally selected. For the optimal selection of key, a new Improved Dragonfly Algorithm (IDA) is introduced. Finally, the algorithmic analysis is carried out by varying parameters like enemy distraction weight e and food attraction weight f  of the proposed algorithm

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%.

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.

Privacy preservation problems in online social networks

2012 ◽  
Author(s):  
Marius Kalinauskas
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Privacy Preservation
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