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.

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 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.

scholarly journals Past Decade Work Done on Cubosomes and its Factorial Design: A Fast Track Information for Researchers

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Y Shravani ◽  
Ahad Hindustan Abdul ◽  
Haranath Chinthaginjala ◽  
Poojitha Bhumireddy gari ◽  
Rahamathulla Syed
Keyword(s):  
Drug Delivery ◽  
Factorial Design ◽  
Rational Design ◽  
Prolonged Release ◽  
Past Work ◽  
The Past ◽  
Wide Range ◽  
Current Scenario ◽  
Work Done ◽  
Nanoparticulate Systems

The objective of this review is to explore the past work done on Cubosomes as drug delivery systems by factorial design. Cubosomes are Nanoparticulate systems made of amphiphilic lipids at a certain percentage, known as liquid crystals. They have tightly packed honeycomb structures twisted into 3D bilayers. Cubosomes can capture all categories of the lipophilic, hydrophilic, and amphiphilic substances irrespective of their affinity, by that they fit for delivering all range of drugs with ease. Many works in recent days are concentrating on Cubosomes for drug delivery, as it suits all ranges of drugs without much difficulty. Cubosomes acts as a carrier in drug delivery for a wide range of drugs and protects them from degradation issues like hydrolysis, oxidation, and others. Moreover, numerous studies have established the benefits of Cubosomes in nanotechnology, prolonged-release, and also enhanced bioavailability. This article reviews about the past work done on Cubosomes using factorial design. Additionally, many studies need to be performed for the optimization of Cubosomes for artificial cells, and biosensors, etc. Moreover, the rational design of Cubosomes for biomedical applications need to be developed. A widespread literature assessment revealed that many reviews and research attempts were made on Cubosomes, but no review article is still available in bringing the attempts made on Cubosomes by factorial design on a single platform. The factorial approach is used to optimize the formulation, which is acceptable and used in the current scenario in optimizing the formulations. So, the authors made widespread work by referring to peer-review journals, periodicals, magazines, and succeeded in bringing work done on Cubosomes in the last ten years by using factorial design. The study concludes and gives a quick reference to the young researchers to get literature on earlier successive attempts done on Cubosomes by factorial design.

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.

T-Closeness Slicing: A New Privacy-Preserving Approach for Transactional Data Publishing

2018 ◽  
Vol 30 (3) ◽  
pp. 438-453 ◽  
Author(s):  
Mingzheng Wang ◽  
Zhengrui Jiang ◽  
Yu Zhang ◽  
Haifang Yang
Keyword(s):  
Privacy Preserving ◽  
Data Publishing ◽  
Transactional Data

scholarly journals Privacy-Preserving Trajectory Data Publishing by Dynamic Anonymization with Bounded Distortion

2021 ◽  
Vol 10 (2) ◽  
pp. 78
Author(s):  
Songyuan Li ◽  
Hui Tian ◽  
Hong Shen ◽  
Yingpeng Sang
Keyword(s):  
Privacy Preservation ◽  
Privacy Preserving ◽  
Data Publishing ◽  
Published Data ◽  
Trajectory Data ◽  
Data Utilization ◽  
Data Set ◽  
Guangzhou City ◽  
Bounded Distortion ◽  
Privacy Breaches

Publication of trajectory data that contain rich information of vehicles in the dimensions of time and space (location) enables online monitoring and supervision of vehicles in motion and offline traffic analysis for various management tasks. However, it also provides security holes for privacy breaches as exposing individual’s privacy information to public may results in attacks threatening individual’s safety. Therefore, increased attention has been made recently on the privacy protection of trajectory data publishing. However, existing methods, such as generalization via anonymization and suppression via randomization, achieve protection by modifying the original trajectory to form a publishable trajectory, which results in significant data distortion and hence a low data utility. In this work, we propose a trajectory privacy-preserving method called dynamic anonymization with bounded distortion. In our method, individual trajectories in the original trajectory set are mixed in a localized manner to form synthetic trajectory data set with a bounded distortion for publishing, which can protect the privacy of location information associated with individuals in the trajectory data set and ensure a guaranteed utility of the published data both individually and collectively. Through experiments conducted on real trajectory data of Guangzhou City Taxi statistics, we evaluate the performance of our proposed method and compare it with the existing mainstream methods in terms of privacy preservation against attacks and trajectory data utilization. The results show that our proposed method achieves better performance on data utilization than the existing methods using globally static anonymization, without trading off the data security against attacks.

Privacy Preserving Big Data Publishing

2018 ◽  
pp. 47-70 ◽  
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.

A Survey on Privacy Preserving Dynamic Data Publishing

2018 ◽  
Vol 8 (4) ◽  
pp. 1-20 ◽  
Author(s):  
Salheddine Kabou ◽  
Sidi mohamed Benslimane ◽  
Mhammed Mosteghanemi
Keyword(s):  
Data Privacy ◽  
Privacy Preservation ◽  
Personal Information ◽  
Privacy Preserving ◽  
Data Publishing ◽  
Future Research ◽  
Research Directions ◽  
Dynamic Data ◽  
Practical Applications ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document