Privacy Preserving Data Mining: Techniques, Classification and Implications - A Survey

Shah, Amita; Gulati, Ravi

doi:10.5120/ijca2016909006

Cited by 22 publications

(27 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, data security should be correlated with data privacy because the former is a requirement of the latter. Privacy is specific, and can be achieved by hiding people's identity or screening personal information that may result in the people's recognition [25].…”

Section: Ppdmmentioning

confidence: 99%

“…PPDM has recently garnered considerable interest among academics and designers. Consequently, several methods have been developed to protect privacy or far-reaching policies have been imposed for sensitive data protection [12], [21], [25]. The form of privacy varies depending on the data used and the way they are used; hence, many methods are used to provide privacy [25].…”

Section: Ppdmmentioning

confidence: 99%

“…Consequently, several methods have been developed to protect privacy or far-reaching policies have been imposed for sensitive data protection [12], [21], [25]. The form of privacy varies depending on the data used and the way they are used; hence, many methods are used to provide privacy [25]. At present, no existing generic solutions can handle all privacy issues regarding the protection of sensitive information from unwanted disclosure while simultaneously preserving the utility of the data.…”

Section: Ppdmmentioning

confidence: 99%

See 2 more Smart Citations

Comprehensive Survey on Big Data Privacy Protection

et al. 2020

View full text Add to dashboard Cite

In recent years, the ever-mounting problem of Internet phishing has been threatening the secure propagation of sensitive data over the web, thereby resulting in either outright decline of data distribution or inaccurate data distribution from several data providers. Therefore, user privacy has evolved into a critical issue in various data mining operations. User privacy has turned out to be a foremost criterion for allowing the transfer of confidential information. The intense surge in storing the personal data of customers (i.e., big data) has resulted in a new research area, which is referred to as privacy-preserving data mining (PPDM). A key issue of PPDM is how to manipulate data using a specific approach to enable the development of a good data mining model on modified data, thereby meeting a specified privacy need with minimum loss of information for the intended data analysis task. The current review study aims to utilize the tasks of data mining operations without risking the security of individuals' sensitive information, particularly at the record level. To this end, PPDM techniques are reviewed and classified using various approaches for data modification. Furthermore, a critical comparative analysis is performed for the advantages and drawbacks of PPDM techniques. This review study also elaborates on the existing challenges and unresolved issues in PPDM. INDEX TERMS Security, big data, privacy protection, privacy-preserving data mining.

show abstract

Section: Ppdmmentioning

confidence: 99%

Section: Ppdmmentioning

confidence: 99%

Section: Ppdmmentioning

confidence: 99%

See 1 more Smart Citation

Comprehensive Survey on Big Data Privacy Protection

et al. 2020

View full text Add to dashboard Cite

show abstract

“…It is also important to design good metrics that can better reflect the properties of a PPDMalgorithm, and to develop benchmark databases for testing all types of PPDM algorithms. Alpa Shah, Ravi Gulati [1], in the article have tried to classify the PPDM techniques available in the literature and showed its implications best suited under various scenarios. Currently no such technique that provides the best solutions under different scenarios exists.…”

Section: IImentioning

confidence: 99%

Encryption based Privacy Preservation on Big Data using Dynamic Data Encryption Strategy

P¹

2019

IJRASET

View full text Add to dashboard Cite

Big data is termed as huge volume of both structured and unstructured dataset. When dealing with these huge dataset several challenges are encountered by the users such as analysis, capture, storage, search, transfer, sharing, and visualization. To handle these complex data sets numerous technologies have been developed. With the developing technologies and all its connected devices, it is predicted that vast amount of data is produced in the last couple of years. There are many algorithms available for clustering the big data. This section focus on the density based clustering algorithms and its related works. Technology is enhancing each and every day especially in the field of Information Technology and data is very momentous elements. The large volume of data generated through devices is a major obstacle to handle in real time. The accomplishment of data ciphering is a crucial problem during the data progression and dissemination. In order to achieve pursuance, many application disregards data encryption. This article represents a concern about data privacy and suggests a novel data encryption approach known as Dynamic Data Encryption Strategy (DDES).

show abstract

“…In this paper, privacy preserving techniques [1], [6], [7], has been classified based on the data lifecycle phases such as data collection, data publishing, and at the output of data mining.…”

Section: Types Of Ppdm Techniquesmentioning

confidence: 99%

An Enhanced Approach for Privacy Preserving Data Mining (PPDM)

2018

IJRTER

View full text Add to dashboard Cite

Abstract-With the development of network, data collection and storage technology, the use and sharing of large amounts of data has become possible. Once the data and information accumulated, it will become the wealth of information. However, traditional data mining techniques and algorithms directly operated on the original data set, which will cause the leakage of privacy data. At the same time, large amounts of data implicate the sensitive knowledge that their disclosure cannot be ignored to the competitiveness of enterprise. In order to overcome these problems, Privacy Preserving Data Mining (PPDM) techniques are developed. Traditional PPDM techniques suffer from different types of attacks and loss of information. In this paper an alternative method was proposed which provides less information loss and more privacy.

show abstract

Privacy Preserving Data Mining: Techniques, Classification and Implications - A Survey

Cited by 22 publications

References 51 publications

Comprehensive Survey on Big Data Privacy Protection

Comprehensive Survey on Big Data Privacy Protection

Encryption based Privacy Preservation on Big Data using Dynamic Data Encryption Strategy

An Enhanced Approach for Privacy Preserving Data Mining (PPDM)

Contact Info

Product

Resources

About