Privacy Preservation in Data Mining for Health Care

The trends of data mining on healthcare data for improving medical services have increased because of the electronic healthcare record(EHR) system, which collects a massive amount of data on a daily basis. In the current scenario, hospital maintains its EHR system and stores the detailed information of patients. Data mining for healthcare improvement requires the data from all the EHR systems located at a different location to be stored at the central data mining server. Collection of healthcare data at some untrusted central data mining server raises privacy threats. Healthcare data contains patients’ private information and sharing this information for data mining creates privacy issues. Most of the previous research either focused on k-anonymity technique which causes information loss and decreases data mining accuracy or privacy preserving data mining which is focused on only specific data mining technique. We adopt source anonymous technique as privacy preserving scheme and present a novel scheme for healthcare data collection and mining in this paper. Our scheme collects data from all EHR systems without any information loss and stores at a single central data mining server, also ensuring privacy is preserved. Central data mining server helps to analyze the collected data with different data mining techniques (Association rule mining, Classification, Clustering, etc.) without the involvement of EHR systems. Our scheme is collusion resilient against central data mining server and EHR systems. Theoretical and experimental analysis show the efficiency of our scheme in terms of computation and communication cost. The experimental results using Heart disease dataset show the advantage to EHR systems using the proposed approach in terms of disease prediction accuracy.

Chapter 10 - Techniques for Privacy Preserving Data Publication in the Cloud for Smart City Applications

In view of present advancements in computing, with the development of distributed environment, many problems have to deal with distributed input data where individual data privacy is the most important issue to be addressed, for the concern of data owner by extending the privacy preserving notion to the original learning algorithms. Privacy Preserving Data Mining has become an active research area in addressing various privacy issues while bringing out solutions for them. There has been lot of progress in developing secure algorithms and models, able to preserve privacy using various data mining techniques like association, classification and clustering, where as importance of privacy preserving techniques applied for learning algorithms related to neural networks for mining problems are still in infancy. Our work in this paper focused on preserving privacy of an individual, using self organizing map (SOM) adopted for collaborative clustering of distributed data between multiple parties. We present Privacy Preserving Collaborative Clustering method using SOM (PPCSOM) for Horizontal Data Distribution, which allows multiple parties perform clustering in a collaborative approach using SOM neural network, without revealing their data directly to each other, in order to preserve privacy of all parties. Our simulation results shows that implementation of PPCSOM method achieves comparable accuracy and assured Privacy than the original non privacy preserving SOM algorithm.

Privacy preservation of online user behavior data is important aspect in data mining, as of late, the study of privacy preserving data mining or networking has been studied mainly due to the extensive proliferation of confidential data on the internet. As online social networking sites attract thousands of million people to use it every day for different purpose, due to this lots of data is being generated every day on internet. Online user behavior analysis and security is becoming increasingly important, as it offers valuable information which can be useful for research purpose or analyst to develop better e-commerce strategies. However, it raises significant privacy concern of users data. Data aggregation scheme is generally used for privacy concern. In this paper, for privacy preservation of multiple online users behavior data, we have studied and analyzed multiple techniques. The main objective behind this survey paper is to percepting the existing privacy preservation and analysis technique of user behavior data and to find a better techniques and algorithms as a way to offer excessive efficiency and scalability. Subsequently, several methods for the privacy preservation and analysis have been suggested to attain the highest relevance.

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.

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

With the rapid development of computer technology and the popularity of the network, database scale, scope and depth of the constantly expanding, which has accumulated vast amounts of different forms of stored data. The use of data mining technology can access valuable information from a lot of data. Privacy preserving has been one of the greater concerns in data mining. Privacy preserving data mining has a rapid development in a short year. But it still faces many challenges in the future. A number of methods and techniques have been developed for privacy preserving data mining. This paper analyzed the representative techniques for privacy preservation. Finally the present problems and directions for future research are discussed.

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.

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.

In this survey, we collect the related information that demonstrate the importance of data mining in healthcare. As the amount of collected health data is increasing significantly every day, it is believed that a strong analysis tool that is capable of handling and analyzing large health data is essential. Analyzing the health datasets gathered by electronic health record (EHR) systems, insurance claims, health surveys, and other sources, using data mining techniques is very complex and is faced with very specific challenges, including data quality and privacy issues. However, the applications of data mining in healthcare, advantages of data mining techniques over traditional methods, special characteristics of health data, and new health condition mysteries have made data mining very necessary for health data analysis.

Optimized key generation-based privacy preserving data mining model for secure data publishing

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

Maintaining data mining accuracy on distorted datasets is an important issue in privacy preserving data mining. Using matrix approximation, we propose several efficient and flexible techniques to address this issue, and utilize unique characteristics of matrix factorization to maintain data pattern. We use the support vector machine classification to compare accuracy maintenance after data distortion by different methods. With better performance than some classical data perturbation approaches, nonnegative matrix factorization and singular value decomposition are considered to be promising techniques for privacy preserving data mining. Experimental results demonstrate that mining accuracy on the distorted data used these methods is almost as good as that on the original data, with added property of privacy preservation. It indicates that the matrix factorization-based data distortion schemes perturb only confidential attributes to meet privacy requirements while preserving general data pattern for knowledge extraction.

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.

Problem of finding frequent patterns has long been studied because it is very essential to data mining tasks such as association rule analysis, clustering, and classification analysis. Privacy preserving data mining is another important issue for this domain since most users do not want their private information to leak out. In this paper, we proposed an efficient approach for mining maximal frequent patterns from a large transactional database with privacy preserving capability. As for privacy preserving, we utilized prime number based data transformation method. We also developed a noble algorithm for mining maximal frequent patterns based on lattice structure. Extensive performance analysis shows the effectiveness of our approach.