A Comparative Survey on Privacy Preservation and Privacy Measuring Techniques in Data Publishing

2018 ◽  
Author(s):  
Atul Kumar ◽  
Manasi Gyanchandani
Keyword(s):  
Privacy Preservation ◽  
Data Publishing ◽  
Measuring Techniques ◽  
Comparative Survey

Mutual Correlation-Based Anonymization for Privacy Preserving Medical Data Publishing

2018 ◽  
pp. 304-319
Author(s):  
Ashoka Kukkuvada ◽  
Poornima Basavaraju
Keyword(s):  
Privacy Preservation ◽  
Information Gain ◽  
Personal Data ◽  
Privacy Preserving ◽  
Medical Data ◽  
Innovative Approach ◽  
Data Publishing ◽  
Mutual Correlation ◽  
Data Utility ◽  
Data Elements

Currently the industry is focused on managing, retrieving, and securing massive amounts of data. Hence, privacy preservation is a significant concern for those organizations that publish/share personal data for vernacular analysis. In this chapter, the authors presented an innovative approach that makes use of information gain of the quasi attributes with respect to sensitive attributes for anonymizing the data, which gives the fruitfulness of an attribute in classifying the data elements, which is a two-way correlation among attributes. The authors show that the proposed approach preserves better data utility and has lesser complexity than former methods.

Privacy Preserving Approaches for Online Social Network Data Publishing

2021 ◽  
pp. 119-132
Author(s):  
Kamalkumar Macwan ◽  
Sankita Patel
Keyword(s):  
Social Network ◽  
Privacy Preservation ◽  
Differential Privacy ◽  
Online Social Network ◽  
Data Publishing ◽  
Network Data ◽  
User Privacy ◽  
Social Network Data ◽  
Huge Data ◽  
Popularity Prediction

Recently, the social network platforms have gained the attention of people worldwide. People post, share, and update their views freely on such platforms. The huge data contained on social networks are utilized for various purposes like research, market analysis, product popularity, prediction, etc. Although it provides so much useful information, it raises the issue regarding user privacy. This chapter discusses the various privacy preservation methods applied to the original social network dataset to preserve privacy against attacks. The two areas for privacy preservation approaches addressed in this chapter are anonymization in social network data publication and differential privacy in node degree publishing.

Privacy Preserving Big Data Publishing

2021 ◽  
pp. 1281-1298
Author(s):  
Nancy Victor ◽  
Daphne Lopez
Keyword(s):  
Big Data ◽  
Language Processing ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Personal Data ◽  
Privacy Preserving ◽  
Data Publishing ◽  
Time Data ◽  
Digital World ◽  
Distributed Framework

Data privacy plays a noteworthy part in today's digital world where information is gathered at exceptional rates from different sources. Privacy preserving data publishing refers to the process of publishing personal data without questioning the privacy of individuals in any manner. A variety of approaches have been devised to forfend consumer privacy by applying traditional anonymization mechanisms. But these mechanisms are not well suited for Big Data, as the data which is generated nowadays is not just structured in manner. The data which is generated at very high velocities from various sources includes unstructured and semi-structured information, and thus becomes very difficult to process using traditional mechanisms. This chapter focuses on the various challenges with Big Data, PPDM and PPDP techniques for Big Data and how well it can be scaled for processing both historical and real-time data together using Lambda architecture. A distributed framework for privacy preservation in Big Data by combining Natural language processing techniques is also proposed in this chapter.

Enhancing Privacy Preservation in Speech Data Publishing

2020 ◽  
Vol 7 (8) ◽  
pp. 7357-7367
Author(s):  
Guanglin Zhang ◽  
Sifan Ni ◽  
Ping Zhao
Keyword(s):  
Privacy Preservation ◽  
Data Publishing ◽  
Speech Data

Privacy Preserving Anonymization Schemes-On Transaction Data Publishing

2020 ◽  
Vol 17 (9) ◽  
pp. 4623-4626
Author(s):  
Nisha Nehra ◽  
Suneet Kumar
Keyword(s):  
Privacy Preservation ◽  
Privacy Preserving ◽  
Data Publishing ◽  
Transaction Data ◽  
Past Work ◽  
The Past ◽  
Identity Disclosure ◽  
Comprehensive Survey ◽  
Work Done ◽  
Transactional Data

Now days, due to the sheer amount of data, its complexity and the rate at which it is generated, traditional algorithms that are present so far for the privacy preservation of relation data publishing are not capable enough to ensure privacy as efficiently for transactional data also. From last two decades the interest also increases to provide better data preserving schemes for data publishing. There are a number of algorithms, schemes, models and techniques in the literature that ensure privacy against identity disclosure and attribute disclosure attacks. This paper is a comprehensive survey of the past work done in the field of anonymization to provide privacy against transactional data publishing.

A Survey on Privacy Preserving Dynamic Data Publishing

2021 ◽  
pp. 1635-1657
Author(s):  
Salheddine Kabou ◽  
Sidi mohamed Benslimane ◽  
Mhammed Mosteghanemi
Keyword(s):  
Data Privacy ◽  
Privacy Preservation ◽  
Personal Information ◽  
Privacy Preserving ◽  
Data Publishing ◽  
Future Research ◽  
Dynamic Data ◽  
Practical Applications ◽  
New Process ◽  
Future Research Directions

Many organizations, especially small and medium business (SMB) enterprises require the collection and sharing of data containing personal information. The privacy of this data must be preserved before outsourcing to the commercial public. Privacy preserving data publishing PPDP refers to the process of publishing useful information while preserving data privacy. A variety of approaches have been proposed to ensure privacy by applying traditional anonymization models which focused only on the single publication of datasets. In practical applications, data publishing is more complicated where the organizations publish multiple times for different recipients or after modifications to provide up-to-date data. Privacy preserving dynamic data publication PPDDP is a new process in privacy preservation which addresses the anonymization of the data for different purposes. In this survey, the author will systematically evaluate and summarize different studies to PPDDP, clarify the differences and requirements between the scenarios that can exist, and propose future research directions.

Body Sensor Network Processing Mechanism for Micro-Data Security Publishing

2014 ◽  
Vol 1049-1050 ◽  
pp. 1536-1539
Author(s):  
Na Li
Keyword(s):  
Sensor Network ◽  
Data Security ◽  
Privacy Preservation ◽  
Data Publishing ◽  
Challenging Problem ◽  
Patient Privacy ◽  
Micro Data ◽  
Body Sensor Network ◽  
Resources Sharing ◽  
Processing Mechanism

Individuals’ privacy protection when publishing data for research has recently put great attention on data mining and information resources sharing fields. Privacy preservation is an important and challenging problem in micro-data publishing. This paper aimed to find an available directly way protect patient privacy. Processing numeric values which got from body sensor network (BSN). Firstly, we analyze the characteristics of medical data which collected from BSN, and then the records will be grouped according to the Quasi-identifier. The last step is to inspect the diversity of sensitive attributes.

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