Privacy Preserving Data Mining: A Comprehensive Survey (original) (raw)

A Review Study on the Privacy Preserving Data Mining Techniques and Approaches

International Journal of Computer Science and Telecommunications (IJCST) , 2013

With the extensive amount of data stored in databases and other repositories it is very important to develop a powerful and effective mean for analysis and interpretation of such data for extracting the interesting and useful knowledge that could help in decision making. Data mining is such a technique which extracts the useful information from the large repositories. Knowledge discovery in database (KDD) is another name of data mining. Privacy preserving data mining techniques are introduced with the aim of extract the relevant knowledge from the large amount of data while protecting the sensible information at the same time. In this paper we review on the various privacy preserving data mining techniques like data modification and secure multiparty computation based on the different aspects. We also analyze the comparative study of all techniques followed by the future research work.

An Enhanced Approach for Privacy Preserving Data Mining (PPDM)

International Journal of Recent Trends in Engineering and Research, 2018

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.

A Review on Privacy Preserving Data Mining

In organization large amount of data are collected daily and these data are used by the organization for data mining tasks. These data collected may contain sensitive attribute which not disclosed by un-trusted user. Privacy is very important when release the data for sharing purpose or mining. Privacy preserving data mining allow publishing data while same time it protect the sensitive or private data. For privacy preserving there are many technique like k-anonymity, cryptography, blocking based, data Perturbation etc. In this paper, various privacy preserving approaches in data sharing and their merits and demerits are analyzed.

Study and Analysis of Privacy Preserving Data Mining Techniques in Current Scenario

International journal of engineering research and technology, 2018

As with the increasing demand of the data mining techniques the privacy preserving is consider as the important factor. In this paper we discuss to provide the security during data mining technique without compromised the utilization of the data. Individuals are well familiar with the security threats and are averse to share their personal information on network. Because of this the outcome of data mining are negligent. By taking into consideration the privacy factor several techniques were proposed, but these methods are in the state of infancy.The fame of these PPDM techniques is based on the accuracy achieved by these algorithm and performance of the algorithm.Nevertheless there is no such algorithm exist which achieve accuracy as well as better performance. However algorithm those perform better may lack in accuracy factor or vice-versa. In this paper we discuss about various methods for ensuring security in data mining and also explore the direction of the future research work.

IJERT-An Enhanced Approach to Privacy-Preserving in Data Mining and its Techniques

International Journal of Engineering Research and Technology (IJERT), 2015

https://www.ijert.org/an-enhanced-approach-to-privacy-preserving-in-data-mining-and-its-techniques https://www.ijert.org/research/an-enhanced-approach-to-privacy-preserving-in-data-mining-and-its-techniques-IJERTV4IS020595.pdf The Privacy preserving Data mining (PPDM) has been among the important issues of current research that deals with preserving privacy of individual's data over a network. The major area of concern is that non-sensitive data even may deliver sensitive information, including personal information, facts or patterns. In this paper, we present a unique concept of combining different PPDM techniques which provides high level security and integrity to confidential data. This paper mainly highlights the improved results that can be obtained on merging the two different PPDM techniques. One of the latest concept of PPDM called Slicing has also been explained in our paper. It has been observed that slicing preserves better data utility and thus we have tried to merge slicing with one of the best security mechanism that is Cryptography.

An Enhanced Approach to Privacy-Preserving in Data Mining and its Techniques

2015

The Privacy preserving Data mining (PPDM) has been among the important issues of current research that deals with preserving privacy of individual's data over a network. The major area of concern is that non-sensitive data even may deliver sensitive information, including personal information, facts or patterns. In this paper, we present a unique concept of combining different PPDM techniques which provides high level security and integrity to confidential data. This paper mainly highlights the improved results that can be obtained on merging the two different PPDM techniques. One of the latest concept of PPDM called Slicing has also been explained in our paper. It has been observed that slicing preserves better data utility and thus we have tried to merge slicing with one of the best security mechanism that is Cryptography.

An Efficient Approach of Privacy Preserving Data Mining

In many organizations large amount of data are collected. These data are sometimes used by the organizations for data mining tasks. However, the data collected may contain private or sensitive information which should be protected. Privacy protection is an important issue if we release data for the mining or sharing purpose. Privacy preserving data mining techniques allow publishing data for the mining purpose while at the same time preserve the private information of the individuals. Many techniques have been proposed for privacy preservation but they suffer from various types of attacks and information loss. In this paper we proposed an efficient approach for privacy preservation in data mining. Our technique protects the sensitive data with less information loss which increase data usability and also prevent the sensitive data for various types of attack. Data can also be reconstructed using our proposed technique.

Classification of Privacy Preserving Data Mining Algorithms: A Review

Jurnal Elektronika dan Telekomunikasi

Nowadays, data from various sources are gathered and stored in databases. The collection of the data does not give a significant impact unless the database owner conducts certain data analysis such as using data mining techniques to the databases. Presently, the development of data mining techniques and algorithms provides significant benefits for the information extraction process in terms of the quality, accuracy, and precision results. Realizing the fact that performing data mining tasks using some available data mining algorithms may disclose sensitive information of data subject in the databases, an action to protect privacy should be taken into account by the data owner. Therefore, privacy preserving data mining (PPDM) is becoming an emerging field of study in the data mining research group. The main purpose of PPDM is to investigate the side effects of data mining methods that originate from the penetration into the privacy of individuals and organizations. In addition, it gu...

Comprehensive Review On Privacy Preserving Data Mining Techniques And Methods

IJEMR, 2017

Now a day’s information is played major role in decision making in an organization. We are in the world of information processing society. Data is the major valuable resource of any business or organization. There is a huge amount of sensitive data produced by various business operational applications. Sharing information between various sources through authorized channel is an important task. Data Mining is kind knowledge discovery system, through this data can extract from different sources. While sharing information through different channels or extracting information from different external sources, the key factor is protecting data from unauthorized accesses. This paper presents a brief idea about protecting extracted data of data mining system without loss of processing data using Privacy Preserving techniques and its comparison.

A Study Survey of Privacy Preserving Data Mining

Data mining is the extraction of interesting patterns or knowledge from huge amount of data. In recent years, with the explosive development in Internet, data storage and data processing technologies, privacy preservation has been one of the greater concerns in data mining. In recent years, privacy-preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the Internet. A number of methods and techniques have been developed for privacy preserving data mining. This paper provides a wide survey of different privacy preserving data mining algorithms and analyses the representative techniques for privacy preserving data mining, and points out their merits and demerits. Finally the present problems and directions for future research are discussed.