Privacy Preservation In Big Data Research Articles

Scalable Privacy Preservation in Big Data

In the realm of data analytics, big data has welcomed a revolution. Data that was abandoned a few years ago is now seen as a valuable resource. Big data is increasingly widely employed across all aspects of society for knowledge extraction. It is generated by practically all digitization, and it is saved and transmitted over the internet. This dependence on the web approach for massive data raises severe security problems. Due to its vast volume, diversity, and volume, traditional security measures cannot be used to big data. Privacy is also a huge security nightmare since a large dataset appears to contain confidential details. Traditional privacy preservation approaches are utilized to solve privacy difficulties in Big Data, and K-anonymity is the most often used strategy for protecting privacy for data disclosure.

A Technique for Securing Big Data Using K-Anonymization With a Hybrid Optimization Algorithm

The recent techniques built on cloud computing for data processing is scalable and secure, which increasingly attracts the infrastructure to support big data applications. This paper proposes an effective anonymization based privacy preservation model using k-anonymization criteria and Grey wolf-Cat Swarm Optimization (GWCSO) for attaining privacy preservation in big data. The anonymization technique is processed by adapting k- anonymization criteria for duplicating k records from the original database. The proposed GWCSO is developed by integrating Grey Wolf Optimizer (GWO) and Cat Swarm Optimization (CSO) for constructing the k-anonymized database, which reveals only the essential details to the end users by hiding the confidential information. The experimental results of the proposed technique are compared with various existing techniques based on the performance metrics, such as Classification accuracy (CA) and Information loss (IL). The experimental results show that the proposed technique attains an improved CA value of 0.005 and IL value of 0.798, respectively.

A technique for securing big data using k-anonymization with a hybrid optimization algorithm

The recent techniques built on cloud computing for data processing is scalable and secure, which increasingly attracts the infrastructure to support big data applications. This paper proposes an effective anonymization based privacy preservation model using k-anonymization criteria and Grey wolf-Cat Swarm Optimization (GWCSO) for attaining privacy preservation in big data. The anonymization technique is processed by adapting k- anonymization criteria for duplicating k records from the original database. The proposed GWCSO is developed by integrating Grey Wolf Optimizer (GWO) and Cat Swarm Optimization (CSO) for constructing the k-anonymized database, which reveals only the essential details to the end users by hiding the confidential information. The experimental results of the proposed technique are compared with various existing techniques based on the performance metrics, such as Classification accuracy (CA) and Information loss (IL). The experimental results show that the proposed technique attains an improved CA value of 0.005 and IL value of 0.798, respectively.

Privacy Preserving in Big Data: Summary of Classification techniques

Privacy Preserving in Big Data: Summary of Classification techniques

Privacy Preservation in Big Data From the Communication Perspective—A Survey

The advancement of data communication technologies promotes widespread data collection and transmission in various application domains, thereby expanding big data significantly. Sensitive information about individuals, which is typically evident or hidden in data, is prone to various privacy attacks and serious risks of privacy disclosure. Corresponding approaches to data privacy preservation have been proposed to provide mechanisms for preserving data privacy while pubilishing useful information or mining valuable information from sanitized data. In this work, we present a comprehensive survey of privacy preservation in big data from the communication perspective. Specifically, we cover the fundamental privacy-preserving framework and privacy-preserving technologies, particularly differential privacy. We also survey the adaptations and variants of differential privacy for different emerging applications and the challenges to differential privacy. In addition, we provide future research directions about privacy preservation in communication field.

Survey on Privacy Preservation in Big Data for Healthcare Monitoring

This paper represents the prototype of health monitoring system using Big Data. this brings several Advantage to the healthcare industry by way of providing timely patient care services, proactive disease detection, real-time monitoring among others. The major challenge faced by healthcare providers on safeguarding the confidentiality of patients’ data.if confidentiality is not well maintained, people may be unable to share their data. Because security has been investigated as a new dimension, “veracity,” in big data, we aim to exploit new challenges of big data in terms of security and devote our attention toward efficient and privacy-preserving computing in the big data era. Specifically, we primary formalize the basic architecture of big data analytics, identify the corresponding privacy requirements, and introduce an efficient and privacy Triple DES as an example in response to data mining’s efficiency and privacy requirements in the big data era. The results of this research exhibit that the proposed system has better secured database access and maintain privacy for all patient data than the traditional database.

A Novel Approach for Privacy Preservation in Bigdata Using Data Perturbation in Nested Clustering in Apache Spark

A Novel Approach for Privacy Preservation in Bigdata Using Data Perturbation in Nested Clustering in Apache Spark

English

English

Privacy Preservation in Big Data

Privacy Preservation in Big Data

Big data privacy: a technological perspective and review

Big data is a term used for very large data sets that have more varied and complex structure. These characteristics usually correlate with additional difficulties in storing, analyzing and applying further procedures or extracting results. Big data analytics is the term used to describe the process of researching massive amounts of complex data in order to reveal hidden patterns or identify secret correlations. However, there is an obvious contradiction between the security and privacy of big data and the widespread use of big data. This paper focuses on privacy and security concerns in big data, differentiates between privacy and security and privacy requirements in big data. This paper covers uses of privacy by taking existing methods such as HybrEx, k-anonymity, T-closeness and L-diversity and its implementation in business. There have been a number of privacy-preserving mechanisms developed for privacy protection at different stages (for example, data generation, data storage, and data processing) of a big data life cycle. The goal of this paper is to provide a major review of the privacy preservation mechanisms in big data and present the challenges for existing mechanisms. This paper also presents recent techniques of privacy preserving in big data like hiding a needle in a haystack, identity based anonymization, differential privacy, privacy-preserving big data publishing and fast anonymization of big data streams. This paper refer privacy and security aspects healthcare in big data. Comparative study between various recent techniques of big data privacy is also done as well.

Privacy Preservation in Big Data using K-Anonymity Algorithm with Privacy Key

Privacy Preservation in Big Data using K-Anonymity Algorithm with Privacy Key

Combination of Access Control and De-Identification for Privacy Preserving in Big Data

Despite of its emergence and advantages in various domains, big data still suffers from major disadvantages. Timeless, scalability, and privacy are the main problems that hinder the advance of big data. Privacy preserving has become a wide search era within the scientific community. This paper covers the problem of privacy preserving over big data by combining both access control and data de-identification techniques in order to provide a powerful system. The aim of this system is to carry on all big data properties (volume, variety, velocity, veracity, and value) to ensure protection of users' identities. After many experiments and tests, our system shows high efficiency on detecting and hiding personal information while maintaining the utility of useful data. The remainder of this report is addressed in the presentation of some known works over a privacy preserving domain, the introduction of some basic concepts that are used to build our approach, the presentation of our system, and finally the display and discussion of the main results of our experiments.

Scalable Privacy Preservation in Big Data a Survey

Abstract Cloud computing provides flexible infrastructure and high storage capacity for BigData applications. The MapReduce framework is most preferable for processing huge volume of unstructured data set in BigData. Increase in data volume leads to flexible and scalable privacy preservation of such dataset over the MapReduce framework is BigData applications. A survey have been taken for the MapReduce framework based big data privacy preservation in Cloud environment. Existing approaches employ local recording anonymization for privacy preserving where data are processed for analysis, mining and sharing. The proposed work focus on Global recording anonymization for preserving data privacy over BigData using MapReduce on Cloud environment.

Privacy Preservation in Big Data

Big data has brought a revolution in the world of data analytics. Data that was discarded a few years back is now considered a powerful asset. Big data is now being extensively used for knowledge discovery by all sectors of society. It is produced by almost all digital processes and is stored, shared on web. This reliance of big data on web model poses serious security concerns. Traditional security methods cannot be applied to big data due to its large volume, variety and volume. Also since big data contains person specific information, privacy is a major security concern. The three important privacy preservation methods are: data anonymization, notice and consent and differential privacy. In this paper we discuss these privacy preservation methods for big data and how differential privacy is a better solution for big data privacy.