A survey on privacy preserving 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 algorithmic techniques have been designed for privacy-preserving data mining. In this paper, we provide a review of the state-of-the-art methods for privacy. We discuss methods for randomization, k-anonymization, and distributed privacy-preserving data mining. We also discuss cases in which the output of data mining applications needs to be sanitized for privacy-preservation purposes. We discuss the computational and theoretical limits associated with privacy-preservation over high dimensional data sets.

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.

2014

Privacy preserving data mining deals with hiding an individual’s sensitive identity without sacrificing the usability of data. It has become a very important area of concern but still this branch of research is in its infancy .People today have become well aware of the privacy intrusions of their sensitive data and are very reluctant to share their information. The major area of concern is that non-sensitive data even may deliver sensitive information, including personal information, facts or patterns. Several techniques of privacy preserving data mining have been proposed in literature. In this paper, we have studied all these state of art techniques. A tabular comparison of work done by different authors is presented. In our future work we will work on a hybrid of these techniques to preserve the privacy of sensitive data. Keywords-‐ data mining; privacy preserving; sensitive attributes; privacy; privacy preserving techniques.

Crossroads, 2009

As it becomes evident, there exists an extended set of application scenarios in which information or knowledge derived from the data must be shared with other (possibly untrusted) entities. The sharing of data and/or knowledge may come at a cost to privacy, primarily due to two reasons:

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.

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.

Data mining is the extraction of the important patterns or information from large amount of data, which is used for decision making in future work. But the process of data collection and data dissemination may cause the serious damage for any organization, however may causes the privacy concerns. Sensitive or personal information of individuals, industries and organization must be kept private before it is shared or published. Thus privacy preserving data mining has become an important issue to efficiently hide sensitive information. Many numbers of methods and techniques have been proposed for privacy preserving data mining for hiding sensitive information. In this paper we provides our own overview which has taken from previous paper on privacy preserving data mining.

ACM Sigmod …, 2004

We provide here an overview of the new and rapidly emerging research area of privacy preserving data mining. We also propose a classification hierarchy that sets the basis for analyzing the work which has been performed in this context. A detailed review of the work accomplished in this area is also given, along with the coordinates of each work to the classification hierarchy. A brief evaluation is performed, and some initial conclusions are made.

2008

Data anonymization is of increasing importance for allowing sharing of individual data for a variety of data analysis and mining applications. Most of existing work on data anonymization optimizes the anonymization in terms of data utility typically through one-size-fits-all measures such as data discernibility. Our primary viewpoint in this paper is that each target application may have a unique need of the data and the best way of measuring data utility is based on the analysis task for which the anonymized data will ultimately be used. We take a top-down analysis of typical application scenarios and derive applicationoriented anonymization criteria. We propose a prioritized anonymization scheme where we prioritize the attributes for anonymization based on how important and critical they are to the application needs. Finally, we present preliminary results that show the benefits of our approach.

As people of every walk of life are using Internet for various purposes there is growing evidence of proliferation of sensitive information. Security and privacy of data became an important concern. For this reason privacy preserving data mining (PPDM) has been an active research area. PPDM is a process discovering knowledge from voluminous data while protecting sensitive information. In this paper we explore the present state-of-the-art of secure and privacy preserving data mining algorithms or techniques which will help in real world usage of enterprise applications. The techniques discussed include randomized method, k-Anonymity, l-Diversity, t-Closeness, m-Privacy and other PPDM approaches. This paper also focuses on SQL injection attacks and prevention measures. The paper provides research insights into the areas of secure and privacy preserving data mining techniques or algorithms besides presenting gaps in the research that can be used to plan future research.