scholarly journals Data Privacy for Big Data Publishing Using Newly Enhanced PASS Data Mining Mechanism

Data Mining ◽  
2018 ◽  
Author(s):  
Priyank Jain ◽  
Manasi Gyanchandani ◽  
Nilay Khare
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Privacy ◽  
Data Publishing

scholarly journals Misusability Measure Based Sanitization of Big Data for Privacy Preserving MapReduce Programming

2018 ◽  
Vol 8 (6) ◽  
pp. 4524
Author(s):  
D. Radhika ◽  
D. Aruna Kumari
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Privacy ◽  
Hybrid Approach ◽  
Privacy Preserving ◽  
Data Publishing ◽  
Distributed Data Mining ◽  
Distributed Data ◽  
Public Cloud ◽  
Sensitive Data

Leakage and misuse of sensitive data is a challenging problem to enterprises. It has become more serious problem with the advent of cloud and big data. The rationale behind this is the increase in outsourcing of data to public cloud and publishing data for wider visibility. Therefore Privacy Preserving Data Publishing (PPDP), Privacy Preserving Data Mining (PPDM) and Privacy Preserving Distributed Data Mining (PPDM) are crucial in the contemporary era. PPDP and PPDM can protect privacy at data and process levels respectively. Therefore, with big data privacy to data became indispensable due to the fact that data is stored and processed in semi-trusted environment. In this paper we proposed a comprehensive methodology for effective sanitization of data based on misusability measure for preserving privacy to get rid of data leakage and misuse. We followed a hybrid approach that caters to the needs of privacy preserving MapReduce programming. We proposed an algorithm known as Misusability Measure-Based Privacy serving Algorithm (MMPP) which considers level of misusability prior to choosing and application of appropriate sanitization on big data. Our empirical study with Amazon EC2 and EMR revealed that the proposed methodology is useful in realizing privacy preserving Map Reduce programming.

Privacy Preserving Data Mining on Unstructured Data

2017 ◽  
pp. 167-190
Author(s):  
Trupti Vishwambhar Kenekar ◽  
Ajay R. Dani
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Structure Data ◽  
Data Privacy ◽  
Differential Privacy ◽  
Unstructured Data ◽  
Map Reduce ◽  
Individual Data ◽  
Data Set ◽  
Privacy Preserving Data Mining

As Big Data is group of structured, unstructured and semi-structure data collected from various sources, it is important to mine and provide privacy to individual data. Differential Privacy is one the best measure which provides strong privacy guarantee. The chapter proposed differentially private frequent item set mining using map reduce requires less time for privately mining large dataset. The chapter discussed problem of preserving data privacy, different challenges to preserving data privacy in big data environment, Data privacy techniques and their applications to unstructured data. The analyses of experimental results on structured and unstructured data set are also presented.

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 Information Security in Big Data: Privacy and Data Mining

IEEE Access ◽  
2014 ◽  
Vol 2 ◽  
pp. 1149-1176 ◽  
Author(s):  
Lei Xu ◽  
Chunxiao Jiang ◽  
Jian Wang ◽  
Jian Yuan ◽  
Yong Ren
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Information Security ◽  
Data Privacy ◽  
Big Data Privacy

scholarly journals Research on Privacy Protection Technology of Mobile Social Network Based on Data Mining under Big Data

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Jiawen Du ◽  
Yong Pi
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Big Data ◽  
Private Information ◽  
Privacy Protection ◽  
Differential Privacy ◽  
Study Data ◽  
Data Publishing ◽  
Data Mining Algorithm ◽  
Data Mining Algorithms

With the advent of the era of big data, people’s lives have undergone earth-shaking changes, not only getting rid of the cumbersome traditional data collection but also collecting and sorting information directly from people’s footprints on social networks. This paper explores and analyzes the privacy issues in current social networks and puts forward the protection strategies of users’ privacy data based on data mining algorithms so as to truly ensure that users’ privacy in social networks will not be illegally infringed in the era of big data. The data mining algorithm proposed in this paper can protect the user’s identity from being identified and the user’s private information from being leaked. Using differential privacy protection methods in social networks can effectively protect users’ privacy information in data publishing and data mining. Therefore, it is of great significance to study data publishing, data mining methods based on differential privacy protection, and their application in social networks.

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.

scholarly journals The Research Trend of Big Data in Education and the Impact of Teacher Psychology on Educational Development During COVID-19: A Systematic Review and Future Perspective

2021 ◽  
Vol 12 ◽  
Author(s):  
Jia Li ◽  
Yuhong Jiang
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Student Performance ◽  
Educational Psychology ◽  
Data Privacy ◽  
Teaching Practice ◽  
Educational Development ◽  
Future Perspective ◽  
Research Trend ◽  
The Impact

The COVID-19 outbreak, along with post-pandemic impact has prompted Internet Plus education to re-examine numerous facets of technology-oriented academic research, particularly Educational Big Data (EBD). However, the unexpected transition from face-to-face offline education to online lessons has urged teachers to introduce educational technology into teaching practice, which has had an overwhelming impact on teachers' professional and personal lives. The aim of this present work is to fathom which research foci construct EBD in a comprehensive manner and how positive psychological indicators function in the technostress suffered by less agentic teachers. To this end, CiteSpace 5.7 and VOSviewer were applied to examine a longitudinal study of the literature from Web of Science Core Collection with the objective of uncovering the explicit patterns and knowledge structures in scientific network knowledge maps. Thousand seven hundred and eight articles concerned with educational data that met the criteria were extracted and analyzed. Research spanning 15 years was conducted to reveal that the knowledge base has accumulated dramatically after many governments' initiatives since 2012 with an accelerating annual growth and decreasing geographic imbalance. The review also identified some influential authors and journals whose effects will continue to have future implications. The authors identified several topical foci such as data mining, student performance, learning environment and psychology, learning analytics, and application. More specifically, the authors identified the scientific shift from data mining application to data privacy and educational psychology, from general scan to specific investigation. Among the conclusions, the results highlighted the important integration of educational psychology and technology during critical periods of educational development.

scholarly journals Research on Design of Fog Computing Optimization Model for Medical Big Data

2021 ◽  
Author(s):  
Baoling Qin
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Deep Learning ◽  
Optimization Model ◽  
Data Privacy ◽  
Fog Computing ◽  
Information Service ◽  
Heterogeneous Data ◽  
Data Redundancy ◽  
Medical Big Data

Targeted at the current issues of communication delay, data congestion, and data redundancy in cloud computing for medical big data, a fog computing optimization model is designed, namely an intelligent front-end architecture of fog computing. It uses the network structure characteristics of fog computing and “decentralized and local” mind-sets to tackle the current medical IoT network’s narrow bandwidth, information congestion, heavy computing burden on cloud services, insufficient storage space, and poor data security and confidentiality. The model is composed of fog computing, deep learning, and big data technology. By full use of the advantages of WiFi and user mobile devices in the medical area, it can optimize the internal technology of the model, with the help of classification methods based on big data mining and deep learning algorithms based on artificial intelligence, and automatically process case diagnosis, multi-source heterogeneous data mining, and medical records. It will also improve the accuracy of medical diagnosis and the efficiency of multi-source heterogeneous data processing while reducing network delay and power consumption, ensuring patient data privacy and safety, reducing data redundancy, and reducing cloud overload. The response speed and network bandwidth of the system have been greatly optimized in the process, which improves the quality of medical information service.

scholarly journals Development of Privacy-Preservation of Big Data with support of Hyperledger Fabric and IPFS

2021 ◽  
Vol 10 (5) ◽  
pp. 2930-2935
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Life Cycle ◽  
Data Privacy ◽  
Privacy Preservation ◽  
File System ◽  
Large Data ◽  
Growth Patterns ◽  
Knowledge Discovery In Databases ◽  
Data Anonymization

Alpesh Vaghela et al., International Journal of Advanced Trends in Computer Science and Engineering, 10(5), September - October 2021, 2930 – 2935 2930 ABSTRACT Academics and industry researchers alike find privacy-preservation of large data to be a very intriguing field of study. Data collection, storage, and processing are the three steps of big data's life cycle. At different stages of the big data life cycle, different privacy and security solutions are used. Many health-care stakeholders are working together to develop a new pattern for safeguarding people from an unknown disease while also promoting economic prosperity. The methods of big data processing and big data analytics will be employed to discover new economic growth patterns. Because the current method of data anonymization leads to data breaches, researchers needed to develop a new way of large data mining or knowledge discovery in databases (KDD), in which numerous parties share their data to identify new patterns. This study introduces a novel way for data mining privacy protection based on Blockchain and the InterPlanetary File System (IPFS) (PPDM). The authors propose leveraging Blockchain and IPFS to create the ChainPPDM approach for preserving big data privacy. The data saved on the blockchain is immutable, transparent, and safe, and it allows for decentralized storage. IPFS is a distributed file system that stores data in a decentralized manner.

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