Kronecker product and bat algorithm-based coefficient generation for privacy protection on cloud

2017 ◽  
Vol 08 (03) ◽  
pp. 1750021 ◽  
Author(s):  
Nandkishor P. Karlekar ◽  
N. Gomathi
Keyword(s):  
Privacy Protection ◽  
Kronecker Product ◽  
Privacy Preservation ◽  
Bat Algorithm ◽  
Original Data ◽  
Data Publishing ◽  
Sensitive Data ◽  
Cloud Data ◽  
Preservation Method ◽  
Client Side

Due to widespread growth of cloud technology, virtual server accomplished in cloud platform may collect useful data from a client and then jointly disclose the client’s sensitive data without permission. Hence, from the perspective of cloud clients, it is very important to take confident technical actions to defend their privacy at client side. Accordingly, different privacy protection techniques have been presented in the literature for safeguarding the original data. This paper presents a technique for privacy preservation of cloud data using Kronecker product and Bat algorithm-based coefficient generation. Overall, the proposed privacy preservation method is performed using two important steps. In the first step, PU coefficient is optimally found out using PUBAT algorithm with new objective function. In the second step, input data and PU coefficient is then utilized for finding the privacy protected data for further data publishing in cloud environment. For the performance analysis, the experimentation is performed with three datasets namely, Cleveland, Switzerland and Hungarian and evaluation is performed using accuracy and DBDR. From the outcome, the proposed algorithm obtained the accuracy of 94.28% but the existing algorithm obtained only the 83.64% to prove the utility. On the other hand, the proposed algorithm obtained DBDR of 35.28% but the existing algorithm obtained only 12.89% to prove the privacy measure.

Use of Purpose and Role Based Access Control Mechanisms to Protect Data Within RDBMS

2020 ◽  
Vol 8 (1) ◽  
pp. 82-91
Author(s):  
Suraj Krishna Patil ◽  
Sandipkumar Chandrakant Sagare ◽  
Alankar Shantaram Shelar
Keyword(s):  
Access Control ◽  
Privacy Preservation ◽  
Original Data ◽  
Sensitive Information ◽  
Role Based Access Control ◽  
Control Mechanisms ◽  
Sensitive Data ◽  
Access To Resources ◽  
Key Factor ◽  
Role Based

Privacy is the key factor to handle personal and sensitive data, which in large chunks, is stored by database management systems (DBMS). It provides tools and mechanisms to access and analyze data within it. Privacy preservation converts original data into some unknown form, thus protecting personal and sensitive information. Different access control mechanisms such as discretionary access control, mandatory access control is used in DBMS. However, they hardly consider purpose and role-based access control in DBMS, which incorporates policy specification and enforcement. The role based access control (RBAC) regulates the access to resources based on the roles of individual users. Purpose based access control (PuBAC) regulates the access to resources based on purpose for which data can be accessed. It regulates execution of queries based on purpose. The PuRBAC system uses the policies of both, i.e. PuBAC and RBAC, to enforce within RDBMS.

Ontology and Hybrid Optimization Based SVNN for Privacy Preserved Medical Data Classification in Cloud

2019 ◽  
Vol 28 (03) ◽  
pp. 1950009 ◽  
Author(s):  
N. Gomathi ◽  
Nandkishor P. Karlekar
Keyword(s):  
Kronecker Product ◽  
Privacy Preservation ◽  
Data Classification ◽  
Bat Algorithm ◽  
Research Area ◽  
Medical Data ◽  
Support Vector ◽  
Medical Data Classification ◽  
Sine Cosine Algorithm ◽  
Significant Attention

One of the emerging technologies, seeking significant attention in the research area is cloud computing. However, privacy is the major concern in the cloud, as it is essential to manage the confidentiality in the data shared. In the first work, the privacy preservation model was developed by newly designed Kronecker product based Bat algorithm. Here, the previous work is extended by developing the classification algorithm for classifying the privacy preserved database. Initially, the Kronecker product based Bat algorithm finds the privacy preserved database from the original medical data. Then, the ontology based features are extracted from the privacy preserved database and given to the data classifier. Here, a classifier, named Whale based Sine Cosine Algorithm with Support Vector Neural Network (WSCA-SVNN), is newly developed for the data classification. The proposed WSCA algorithm helps in optimally choosing the weights for SVNN classifier, and finally, the WSCA-SVNN classifier classifies the medical data. The simulation of the proposed privacy preserved data classification network is done by utilizing the heart disease database. The analysis shows that the proposed WSCA-SVNN classifier scheme achieved an accuracy value of 90.29% during medical data classification.

Personalized trajectory privacy-preserving method based on sensitive attribute generalization and location perturbation

2021 ◽  
Vol 25 (5) ◽  
pp. 1247-1271
Author(s):  
Chuanming Chen ◽  
Wenshi Lin ◽  
Shuanggui Zhang ◽  
Zitong Ye ◽  
Qingying Yu ◽  
...  
Keyword(s):  
Private Information ◽  
Privacy Protection ◽  
Privacy Preservation ◽  
Background Knowledge ◽  
Sensitive Information ◽  
Frequent Patterns ◽  
Trajectory Data ◽  
Preservation Method ◽  
Sensitive Attribute ◽  
Theoretical Analyses

Trajectory data may include the user’s occupation, medical records, and other similar information. However, attackers can use specific background knowledge to analyze published trajectory data and access a user’s private information. Different users have different requirements regarding the anonymity of sensitive information. To satisfy personalized privacy protection requirements and minimize data loss, we propose a novel trajectory privacy preservation method based on sensitive attribute generalization and trajectory perturbation. The proposed method can prevent an attacker who has a large amount of background knowledge and has exchanged information with other attackers from stealing private user information. First, a trajectory dataset is clustered and frequent patterns are mined according to the clustering results. Thereafter, the sensitive attributes found within the frequent patterns are generalized according to the user requirements. Finally, the trajectory locations are perturbed to achieve trajectory privacy protection. The results of theoretical analyses and experimental evaluations demonstrate the effectiveness of the proposed method in preserving personalized privacy in published trajectory data.

scholarly journals Reinforced Social Ant with Discrete Swarm Optimizer for Sensitive Item and Rule Hiding

2019 ◽  
Vol 8 (9) ◽  
pp. 902-908
Keyword(s):  
Data Mining ◽  
Privacy Preservation ◽  
Distributed Databases ◽  
Data Publishing ◽  
Sensitive Information ◽  
Sensitive Data ◽  
Swarm Optimization ◽  
Privacy Preserving Data Mining ◽  
The Past ◽  
Research Areas

In data mining Privacy Preserving Data mining (PPDM) of the important research areas concentrated in recent years which ensures ensuring sensitive information and rule not being revealed. Several methods and techniques were proposed to hide sensitive information and rule in databases. In the past, perturbation-based PPDM was developed to preserve privacy before use and secure mining of association rules were performed in horizontally distributed databases. This paper presents an integrated model for solving the multi-objective factors, data and rule hiding through reinforcement and discrete optimization for data publishing. This is denoted as an integrated Reinforced Social Ant and Discrete Swarm Optimization (RSADSO) model. In RSA-DSO model, both Reinforced Social Ant and Discrete Swarm Optimization perform with the same particles. To start with, sensitive data item hiding is performed through Reinforced Social Ant model. Followed by this performance, sensitive rules are identified and further hidden for data publishing using Discrete Swarm Optimization model. In order to evaluate the RSA-DSO model, it was tested on benchmark dataset. The results show that RSA-DSO model is more efficient in improving the privacy preservation accuracy with minimal time for optimal hiding and also optimizing the generation of sensitive rules.

Slicing based efficient privacy preservation technique with multiple sensitive attributes for safe data distribution

2020 ◽  
pp. 1-8
Author(s):  
G. Murugaboopathi ◽  
V. Gowthami
Keyword(s):  
Privacy Preservation ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Data Publishing ◽  
Sensitive Data ◽  
Security Breaches ◽  
Privacy Threats ◽  
Novel Approach ◽  
Preservation Technique

Privacy preservation in data publishing is the major topic of research in the field of data security. Data publication in privacy preservation provides methodologies for publishing useful information; simultaneously the privacy of the sensitive data has to be preserved. This work can handle any number of sensitive attributes. The major security breaches are membership, identity and attribute disclosure. In this paper, a novel approach based on slicing that adheres to the principle of k-anonymity and l-diversity is introduced. The proposed work withstands all the privacy threats by the incorporation of k-means and cuckoo-search algorithm. The experimental results with respect to suppression ratio, execution time and information loss are satisfactory, when compared with the existing approaches.

scholarly journals Comparative Analysis of Some Efficient Data Security Methods among Cryptographic Techniques for Cloud Data Security

2020 ◽  
Vol 27 (1) ◽  
pp. 81-88
Author(s):  
M.A.T. Abubakar ◽  
A. Aloysius ◽  
Z. Umar ◽  
M. Dauda
Keyword(s):  
Comparative Analysis ◽  
Data Storage ◽  
Data Security ◽  
Data Privacy ◽  
Cloud Service ◽  
Original Data ◽  
Sensitive Data ◽  
Cloud Data ◽  
Data Control ◽  
Encryption Algorithms

The concept of cloud computing model is to grant users access to outsource data from the cloud server without them having to worry about aspects of the hardware and software management. The owner of the data encrypts it before outsourcing to a Cloud Service Provider (CSP) server for effective deployment of sensitive data. Data confidentiality is a demanding task of cloud data protection. Thus, to solve this problem, lots of techniques are needed to defend the shared data. We focus on cryptography to secure the data while transmitting in the network. We deployed Advanced Encryption Standard (AES) used as encryption method for cloud data security, to encrypt the sensitive data which is to be transmitted from sender to receiver in the network and to decrypt so that the receiver can view the original data. Arrays of encryption systems are being deployed in the world of Information Systems by various organizations. In this paper, comparative analysis of some various encryption algorithms in cryptography have been implemented by comparing their performance in terms of stimulated time during Encryption and decryption in the network. Keywords: AES, Data Control, Data Privacy, Data Storage, Encryption Algorithms, Verification.

scholarly journals A noise-based privacy preserving model for Internet of Things

2021 ◽  
Author(s):  
Shelendra Kumar Jain ◽  
Nishtha Kesswani
Keyword(s):  
Internet Of Things ◽  
Privacy Protection ◽  
Privacy Preservation ◽  
Data Access ◽  
Privacy Preserving ◽  
Sensitive Data ◽  
Computational Overhead ◽  
Data Access Control ◽  
Proposed Model ◽  
Control Scheme

AbstractWith the ever-increasing number of devices, the Internet of Things facilitates the connection between the devices in the hyper-connected world. As the number of interconnected devices increases, sensitive data disclosure becomes an important issue that needs to be addressed. In order to prevent the disclosure of sensitive data, effective and feasible privacy preservation strategies are necessary. A noise-based privacy-preserving model has been proposed in this article. The components of the noise-based privacy-preserving model include Multilevel Noise Treatment for data collection; user preferences-based data classifier to classify sensitive and non-sensitive data; Noise Removal and Fuzzification Mechanism for data access and user-customized privacy preservation mechanism. Experiments have been conducted to evaluate the performance and feasibility of the proposed model. The results have been compared with existing approaches. The experimental results show an improvement in the proposed noise-based privacy-preserving model in terms of computational overhead. The comparative analysis indicates that the proposed model without the fuzzifier has around 52–77% less computational overhead than the Data access control scheme and 46–70% less computational overhead compared to the Dynamic Privacy Protection model. The proposed model with the fuzzifier has around 48–73% less computational overhead compared to the Data access control scheme and 31–63% less computational overhead compared to the Dynamic Privacy Protection model. Furthermore, the privacy analysis has been done with the relevant approaches. The results indicate that the proposed model can customize privacy as per the users’ preferences and at the same time takes less execution time which reduces the overhead on the resource constraint IoT devices.

scholarly journals What If Keys Are Leaked? towards Practical and Secure Re-Encryption in Deduplication-Based Cloud Storage

Information ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 142
Author(s):  
Weijing You ◽  
Lei Lei ◽  
Bo Chen ◽  
Limin Liu
Keyword(s):  
Experimental Evaluation ◽  
Cloud Storage ◽  
Security Analysis ◽  
Encrypted Data ◽  
Cloud Data ◽  
Storage Cost ◽  
Outsourced Data ◽  
Proof Of Ownership ◽  
Client Side ◽  
Over Time

By only storing a unique copy of duplicate data possessed by different data owners, deduplication can significantly reduce storage cost, and hence is used broadly in public clouds. When combining with confidentiality, deduplication will become problematic as encryption performed by different data owners may differentiate identical data which may then become not deduplicable. The Message-Locked Encryption (MLE) is thus utilized to derive the same encryption key for the identical data, by which the encrypted data are still deduplicable after being encrypted by different data owners. As keys may be leaked over time, re-encrypting outsourced data is of paramount importance to ensure continuous confidentiality, which, however, has not been well addressed in the literature. In this paper, we design SEDER, a SEcure client-side Deduplication system enabling Efficient Re-encryption for cloud storage by (1) leveraging all-or-nothing transform (AONT), (2) designing a new delegated re-encryption (DRE), and (3) proposing a new proof of ownership scheme for encrypted cloud data (PoWC). Security analysis and experimental evaluation validate security and efficiency of SEDER, respectively.

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