Big data privacy: The datafication of personal information

The use of the patient’s information in biomedical research or healthcare research is increasing rapidly. We are using big data to generate and collect a large amount of personal information of patients. The security of patients individual data have turned into an extraordinary threat as it might prompt spillage of delicate data which can put the patient’s protection in danger. There are various measures which have been taken to protect the data from attack. The relevant paper reviews relevant topics in the context of healthcare research. We will discuss the consequences of big data privacy in healthcare research and a better way to improve the data privacy in healthcare research or biomedical research.

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

International Journal of Information Security and Privacy ◽

10.4018/ijisp.2016010101 ◽

2016 ◽

Vol 10 (1) ◽

pp. 1-27 ◽

Cited By ~ 1

Author(s):

Amine Rahmani ◽

Abdelmalek Amine ◽

Reda Mohamed Hamou

Keyword(s):

Big Data ◽

Access Control ◽

Data Privacy ◽

Scientific Community ◽

High Efficiency ◽

Personal Information ◽

Privacy Preserving ◽

Big Data Privacy ◽

Basic Concepts ◽

Identification Techniques

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.

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International Law and New Challenges to Democracy in the Digital Age: Big Data, Privacy and Interferences with the Political Process

SSRN Electronic Journal ◽

10.2139/ssrn.3430035 ◽

2019 ◽

Author(s):

Dominik Steiger

Keyword(s):

Big Data ◽

International Law ◽

Data Privacy ◽

Political Process ◽

Digital Age ◽

The Political ◽

Big Data Privacy ◽

New Challenges

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Big Data Privacy Preservation Using Two Phase Top-Down Specialization Algorithm with Multidimensional Map Reduce Framework on Hadoop

International Journal of Distributed and Cloud Computing ◽

10.21863/ijdcc/2015.3.2.009 ◽

2015 ◽

Vol 3 (2) ◽

Author(s):

Shalin Eliabeth S. ◽

Sarju S.

Keyword(s):

Big Data ◽

Data Privacy ◽

Privacy Preservation ◽

Experimental Result ◽

Map Reduce ◽

Distributed Environment ◽

Top Down ◽

Two Phase ◽

Data Anonymization ◽

Big Data Privacy

Big data privacy preservation is one of the most disturbed issues in current industry. Sometimes the data privacy problems never identified when input data is published on cloud environment. Data privacy preservation in hadoop deals in hiding and publishing input dataset to the distributed environment. In this paper investigate the problem of big data anonymization for privacy preservation from the perspectives of scalability and time factor etc. At present, many cloud applications with big data anonymization faces the same kind of problems. For recovering this kind of problems, here introduced a data anonymization algorithm called Two Phase Top-Down Specialization (TPTDS) algorithm that is implemented in hadoop. For the data anonymization-45,222 records of adults information with 15 attribute values was taken as the input big data. With the help of multidimensional anonymization in map reduce framework, here implemented proposed Two-Phase Top-Down Specialization anonymization algorithm in hadoop and it will increases the efficiency on the big data processing system. By conducting experiment in both one dimensional and multidimensional map reduce framework with Two Phase Top-Down Specialization algorithm on hadoop, the better result shown in multidimensional anonymization on input adult dataset. Data sets is generalized in a top-down manner and the better result was shown in multidimensional map reduce framework by the better IGPL values generated by the algorithm. The anonymization was performed with specialization operation on taxonomy tree. The experiment shows that the solutions improves the IGPL values, anonymity parameter and decreases the execution time of big data privacy preservation by compared to the existing algorithm. This experimental result will leads to great application to the distributed environment.

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