PRIVACY PRESERVING USING ENSEMBLE CLASSIFICATION FOR HEART DISEASE DATA SETS

A number of privacy preserving techniques have been proposed recently in data mining. In this paper, we provide a review of the state-of-the-art methods for privacy preserving data mining. and discuss methods for randomization, secure multipart computation, and so on. We also make a classification for the privacy preserving data mining technologies, and analyze some works in this field, such as data distortion method for achieving privacy preserving association rule mining. Detailed evaluation criteria of privacy preserving algorithm were illustrated, which include algorithm performance, data utility, and privacy protection degree. Finally, the development of privacy preserving data mining for further directions is given.

With the rapid development of big data and data analysis technology, a large amount of data is collected and used for data mining. However, the prediction accuracy of data mining often fails to meet the needs of third-party data users, and holders may not explicitly and openly share their data. How to share data and handle data privacy issues between entities while ensuring error-free prediction becomes a challenging problem. Therefore, privacy preserving data mining(PPDM) technology was introduced. Among them, the random perturbation method is a direct and effective method to protect data privacy by modifying some sensitive attribute values. This paper proposes a new method of data mining for privacy protection, which is based on SVM machine learning algorithm for data mining and great balances the utility and security of data. JHI and WBC data sets in machine learning database were used in the experiment, and machine learning privacy protection parameters were used for security evaluation. Experiments were conducted to compare NMF and NMFSVD algorithms, and the experiments results show that RNP privacy protection data mining method not only has less error, but also can better protect the privacy of the data set.

Data is increasing rapidly with the vast development of technologies. Mining is mandatory to retrieve knowledge from these data. But, the data may contain sensitive information of the individuals such as medical diagnostic reports which they don’t want to reveal. Privacy Preserving Data Mining (PPDM) helps in this regard to keep sensitive data private as well as preserving the data utility. Besides other PPDM techniques, rotation based perturbation contributes to satisfying both aspects of PPDM i.e., individuals’ privacy and data utility. Also, normalization techniques have a significant impact in the field of PPDM. In this work, the effects of three well-known normalization approaches (i.e., z-score, min-max and decimal scaling) on rotation based perturbation techniques called 2-Dimensional Rotation Transformation (2DRT) and 3-Dimensional Rotation Transformation (3DRT), are analyzed. The effects of normalization over rotation based perturbation techniques are investigated using three benchmark classifiers with five UCI data set. The empirical analysis elucidates that 3DRT provides significant privacy preservation and data utility maintenance than 2DRT for z-score normalization.

A multi-agent system to support evidence based medicine and clinical decision making via data sharing and data privacy

In today's competitive scenario in industries, each organization wants huge profit. A Data Warehouse is a place for storage of reservoir of data from the various organizations in the range of hundreds of gigabytes to terabytes in size. During the course of time, large volumes of data are being gathered continuously in the Data Warehouse Server for further mining operations in order to understand the latest trends and patterns in their business organizations which in turn be exploited by them for increasing their business profits. Organizations want to conceal its sensitive data from the Data Mining Server but still have the aim to obtain accurate data mining results. Privacy Preserving Data Mining is one of the important fields in the data security. This paper deals with Object Oriented Modeling of Privacy Preservation framework in which the privacy preserving technique applied at the DataWarehouseServer. Object Oriented Programming is the latest trend in the software industry due to its various features like reusability of software codes, better user interface, enhancing software security by restricting data access, cheaper production, development and maintenance costs and quicker software development. Authors have modeled the entire Privacy Preserving framework through standard UML diagrams such as use case, class diagram, sequence diagram and Activity diagram by using Rational Software Architect tool. This will facilitate the designers and developers to have a better understanding of the functionality of each and every entity associated with the system during analysis and design phase, before actual implementation. This simplifies the complexity of the entire operation and helps its implementation in a time and cost efficient manner.

Two methods for privacy preserving data mining with malicious participants

Privacy Preservation in Data Mining for Health Care

Privacy preserving rule-based classifier using modified artificial bee colony algorithm

As there are new techniques growing to reveal the hidden information on data, the threat towards those data also increases. Therefore, privacy preservation in data mining is an emerging research area which develops various algorithms to anonymize the data provided for data mining. The existing methodology handles the tradeoff between utility and privacy of data in a more expensive way in terms of execution time. In this paper, a simple Anonymization technique using subclustering is specified which achieves maximum privacy and also utility with minimum execution time. The methodology is explained with algorithm and the results are compared with the baseline method.

Privacy preserving data mining have a rapid development in a short year. But it still faces many challenges in the future. Firstly, the level of privacy has different definitions in different filed. Therefore, the measure of privacy preserving data mining technology protecting private information is not the same. So, it's an urgent issue to present a unified privacy definition and measure. Secondly, the most of research in privacy preserving data mining is presently confined to the theory study.

The main objective of data mining algorithms are to mine previously unknown patterns from the enormous size of data. In current days there is a remarkable expansion in data collection due to the development in the field of information technology. The results, driven by data mining algorithm can be used in many domains such as Image Analysis, Marketing, weather forecasting etc. Some sensitive data is also exposed this is a side effect of the mining algorithm. Here we can preserve the privacy by using PPDM (privacy-preserving data mining) techniques. In this undertaking, fuzzy data transformation technique is proposed for privacy preservation in database surroundings. The triangular fuzzy membership function is a method of fuzzy data transformation to change the original dataset.

Privacy-preserving data mining (PPDM) refers to data mining techniques developed to protect sensitive data while allowing useful information to be discovered from the data. In this article, we review PPDM and present a broad survey of related issues, techniques, measures, applications, and regulation guidelines. We observe that the rapid pace of change in information technologies available to sustain PPDM has created a gap between theory and practice. We posit that without a clear understanding of the practice, this gap will be widening which, ultimately, will be detrimental to the field. We conclude by proposing a comprehensive research agenda intended to bridge the gap relevant to practice and as a reference basis for the future related legislation activities.

Effective data quality management for electronic medical record data using SMART DATA

Efficient perturbation techniques for preserving privacy of multivariate sensitive data

Privacy Preserving Data Mining (PPDM) is an area that deals with data mining techniques that allow the data to be mined while keeping its privacy intact. PPDM comes into play when different parties want to involve in collaborative data mining and wish to collectively mine their data to extract knowledge while keeping their data private. The preservation of privacy of data is also of utmost importance when we want to publish any data as open data. This study includes a detailed review of some of the recent techniques proposed in this area. The biggest challenge in devising any privacy preserving data mining technique is to achieve a good balance between privacy and utility of the privacy preserved data.