Performance Analysis of Various Anonymization Techniques for Privacy Preservation of Sensitive Data

2018 ◽  
pp. 687-693 ◽  
Author(s):  
B. Sreevidya ◽  
M. Rajesh ◽  
T. Sasikala
Keyword(s):  
Performance Analysis ◽  
Privacy Preservation ◽  
Sensitive Data

scholarly journals A Fuzzy Logic based Privacy Preservation Clustering method for achieving K- Anonymity using EMD in dLink Model

2016 ◽  
Vol 12 (12) ◽  
pp. 4601-4610 ◽  
Author(s):  
D. Palanikkumar ◽  
S. Priya ◽  
S. Priya
Keyword(s):  
Data Mining ◽  
Privacy Preservation ◽  
Numerical Data ◽  
Fuzzy Membership Function ◽  
Data Mining Technique ◽  
Sensitive Data ◽  
Sensitive Attribute ◽  
Modification Technique ◽  
Earth Mover ◽  
Preservation Technique

Privacy preservation is the data mining technique which is to be applied on the databases without violating the privacy of individuals. The sensitive attribute can be selected from the numerical data and it can be modified by any data modification technique. After modification, the modified data can be released to any agency. If they can apply data mining techniques such as clustering, classification etc for data analysis, the modified data does not affect the result. In privacy preservation technique, the sensitive data is converted into modified data using S-shaped fuzzy membership function. K-means clustering is applied for both original and modified data to get the clusters. t-closeness requires that the distribution of sensitive attribute in any equivalence class is close to the distribution of the attribute in the overall table. Earth Mover Distance (EMD) is used to measure the distance between the two distributions should be no more than a threshold t. Hence privacy is preserved and accuracy of the data is maintained.

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.

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.

scholarly journals Data Privacy Preservation and Security Approaches for Sensitive Data in Big Data

2021 ◽  
Author(s):  
Rohit Ravindra Nikam ◽  
Rekha Shahapurkar
Keyword(s):  
Data Mining ◽  
Data Analytics ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Large Data ◽  
Research Area ◽  
Data Sets ◽  
Sensitive Information ◽  
Sensitive Data ◽  
Data Mining Techniques

Data mining is a technique that explores the necessary data is extracted from large data sets. Privacy protection of data mining is about hiding the sensitive information or identity of breach security or without losing data usability. Sensitive data contains confidential information about individuals, businesses, and governments who must not agree upon before sharing or publishing his privacy data. Conserving data mining privacy has become a critical research area. Various evaluation metrics such as performance in terms of time efficiency, data utility, and degree of complexity or resistance to data mining techniques are used to estimate the privacy preservation of data mining techniques. Social media and smart phones produce tons of data every minute. To decision making, the voluminous data produced from the different sources can be processed and analyzed. But data analytics are vulnerable to breaches of privacy. One of the data analytics frameworks is recommendation systems commonly used by e-commerce sites such as Amazon, Flip Kart to recommend items to customers based on their purchasing habits that lead to characterized. This paper presents various techniques of privacy conservation, such as data anonymization, data randomization, generalization, data permutation, etc. such techniques which existing researchers use. We also analyze the gap between various processes and privacy preservation methods and illustrate how to overcome such issues with new innovative methods. Finally, our research describes the outcome summary of the entire literature.

A Secure Three Factor Based Authentication Scheme for Telecare Medicine Information System with Privacy Preservation

2022 ◽  
Vol 16 (1) ◽  
pp. 0-0
Keyword(s):  
Information System ◽  
Privacy Preservation ◽  
Chaotic Map ◽  
Security Analysis ◽  
Security And Privacy ◽  
Telecare Medicine Information System ◽  
Medicine Information ◽  
Sensitive Data ◽  
Healthcare Data ◽  
Remote Healthcare

Telecare Medicine Information System (TMIS) is now attracting field for remote healthcare, diagnosis and emergency health services etc. The major objective of this type of system is to provide medical facilities to patients who are critically ill and unable to attend hospitals or put in isolation for observations. A major challenge of such systems is to securely transmit patients' health related information to the medical server through an insecure channel. This collected sensitive data is further used by medical practitioners for diagnosis and treatment purposes. Therefore, security and privacy are essential for healthcare data. In this paper, a robust authentication protocol based on Chebyshev Chaotic map has been proposed for adequate security while transmitting data. The privacy preservation is maintained by a rule set which mainly controls the views. A detailed security analysis was performed for the proposed scheme.

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.

scholarly journals EFURMS: An Efficient Scheme for File Upload and Ranked Multi-Keyword Search over Encrypted Data in Cloud

2021 ◽  
Vol 10 (4) ◽  
pp. 224-231
Author(s):  
Bhavya M ◽  
Thriveni J ◽  
Venugopal K R
Keyword(s):  
Cloud Storage ◽  
Privacy Preservation ◽  
Keyword Search ◽  
Search Algorithm ◽  
Current System ◽  
Sensitive Data ◽  
K Value ◽  
Storage Servers ◽  
Time Required ◽  
Index Tree

Cloud based services provide scalable storage capacities and enormous computing capability to enterprises and individuals to support big data operations in different sectors like banking, scientific research and health care. Therefore many data owners are interested to outsource their data to cloud storage servers due to their huge advantage in data processing. However, as the banking and health records usually contain sensitive data, there are privacy concerns if the data gets leaked to un-trusted third parties in cloud storage. To protect data from leakage, the widely used technique is to encrypt the data before uploading into cloud storage servers. The traditional methods implemented by many authors consumes more time to outsource the data and searching for a document is also time consuming. Sometimes there may be chances of data leakage due to insufficient security. To resolve these issues, in the current VPSearch(VPS) scheme is implemented, which provides features like verifiability of search results and privacy preservation. With its features the current system consumes more time for file uploading and index generation, which slows down the searching process. In the existing VPS scheme time minimization to efficiently search for a particular document is a challenging task on the cloud. To resolve all the above drawbacks, we have designed an index generation scheme using a tree structure along with a search algorithm using Greedy Depth-first technique, that reduces the time for uploading files and file searching time. The newly implemented scheme minimizes the time required to form the index tree file for set of files in the document which are to be uploaded and helps in storing the files in a index tree format. These techniques result in reducing the document upload time and speeding up the process of accessing data efficiently using multi-keyword search with top-'K' value.

scholarly journals A Non-Reversible Privacy Preservation Model for Outsourced High-Dimensional Healthcare Data

2021 ◽  
Author(s):  
Syed Usama Khalid Bukhari ◽  
Anum Qureshi ◽  
Adeel Anjum ◽  
Munam Ali Shah
Keyword(s):  
Privacy Preservation ◽  
Differential Privacy ◽  
Personal Information ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Sensitive Data ◽  
Privacy Concerns ◽  
Healthcare Data ◽  
Pros And Cons ◽  
Privacy Breaches

<div> <div> <div> <p>Privacy preservation of high-dimensional healthcare data is an emerging problem. Privacy breaches are becoming more common than before and affecting thousands of people. Every individual has sensitive and personal information which needs protection and security. Uploading and storing data directly to the cloud without taking any precautions can lead to serious privacy breaches. It’s a serious struggle to publish a large amount of sensitive data while minimizing privacy concerns. This leads us to make crucial decisions for the privacy of outsourced high-dimensional healthcare data. Many types of privacy preservation techniques have been presented to secure high-dimensional data while keeping its utility and privacy at the same time but every technique has its pros and cons. In this paper, a novel privacy preservation NRPP model for high-dimensional data is proposed. The model uses a privacy-preserving generative technique for releasing sensitive data, which is deferentially private. The contribution of this paper is twofold. First, a state-of-the-art anonymization model for high-dimensional healthcare data is proposed using a generative technique. Second, achieved privacy is evaluated using the concept of differential privacy. The experiment shows that the proposed model performs better in terms of utility. </p> </div> </div> </div>

scholarly journals A Non-Reversible Privacy Preservation Model for Outsourced High-Dimensional Healthcare Data

2021 ◽  
Author(s):  
Syed Usama Khalid Bukhari ◽  
Anum Qureshi ◽  
Adeel Anjum ◽  
Munam Ali Shah
Keyword(s):  
Privacy Preservation ◽  
Differential Privacy ◽  
Personal Information ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Sensitive Data ◽  
Privacy Concerns ◽  
Healthcare Data ◽  
Pros And Cons ◽  
Privacy Breaches

<div> <div> <div> <p>Privacy preservation of high-dimensional healthcare data is an emerging problem. Privacy breaches are becoming more common than before and affecting thousands of people. Every individual has sensitive and personal information which needs protection and security. Uploading and storing data directly to the cloud without taking any precautions can lead to serious privacy breaches. It’s a serious struggle to publish a large amount of sensitive data while minimizing privacy concerns. This leads us to make crucial decisions for the privacy of outsourced high-dimensional healthcare data. Many types of privacy preservation techniques have been presented to secure high-dimensional data while keeping its utility and privacy at the same time but every technique has its pros and cons. In this paper, a novel privacy preservation NRPP model for high-dimensional data is proposed. The model uses a privacy-preserving generative technique for releasing sensitive data, which is deferentially private. The contribution of this paper is twofold. First, a state-of-the-art anonymization model for high-dimensional healthcare data is proposed using a generative technique. Second, achieved privacy is evaluated using the concept of differential privacy. The experiment shows that the proposed model performs better in terms of utility. </p> </div> </div> </div>

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