Recent Advances in Preserving Privacy Data Mining

Privacy Preservation in Data Mining (PPDM) including for Privacy Preserving Association Rule Mining (PPARM) has attracted lots of attention in recent research and practice. However, the current method or approach still have drawbacks in the sense that there are trade-offs between efficiency and privacy preservation. This paper describes our work towards providing a new efficient PPARM protocol. We reviewed current literature on PPARM and mapped the methods or approaches involved. As previous research showed that Elliptic Curve Cryptography (ECC) perform better than the other Public Key systems such as RSA and Diffie-Hellman, we will utilize ECC for reducing the computational cost of the new PPARM protocol. In choosing good elliptic curves for ECC, we measured the running time of the key generation for various group of recommended elliptic curves i.e. Brainpool curves (by Brainpool), Prime, C2pnb, C2tnb curves (by ANSI X9.62), Secp curves (by SECG), and PrimeCurve curves (by CDC Group). As the result, Secp curves outperformed all of the other curves on overall average ratio of running time and key size of key generation by 4.4% up to 357.6%.

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.

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.

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 rule-based classifier using modified artificial bee colony algorithm

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.

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 Preservation in Data Mining for Health Care

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.

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.

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.

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

Two methods for privacy preserving data mining with malicious participants

In this modern era of thriving technology, the data being gathered through way of private in addition to public businesses is increasing each day. But in recent times people are more worried about their data and privateness being preserved at the same time as use of their data in other analysis purpose. Thus Privacy-Preserving Data Mining (PPDM) method has been proposed to permit the extraction of understanding from data at the same time as keeping the privateness of people. The primary purpose of our project is on preserving privacy for healthcare records as privateness lacks in Medical data. Privacy-Preserving Data Mining (PPDM) offers with shielding the privacy of individual’s data or sensitive data without the utility of data. Therefore the strategies like anonymization, randomization are used to attain the intention. However, unfortunately anonymization results in certain level of information loss while preserving privacy. In order to overcome this problem, perturbation technique is carried out. Our challenge initiates with cleaning and preprocessing followed by ensemble classification and proceeded with perturbation to attain the goal. This method focuses on preserving privacy by perturbing the sensitive attributes in the Medical data without causing loss to the information in the process

With the growing technology and amount of data, the use of cloud and centralized servers is increasing. When the data of the individuals is stored by the third party it leads to the insecurity of misuse of their information in the individuals. Data Mining is carried on this data stored over cloud to get useful information, analysis and decision making purposes. But along with the process of mining, privacy preservation is the key concern. While performing data mining over large set of data there are chances of privacy violation, loss of information, loss of privacy of individuals. While concentrating only on preserving privacy can increase inefficiency and complexity of data mining techniques. Techniques used for achieving privacy lead to a significant information loss. The methods concerned about information loss generally lack behind in achieving a good level of privacy. Thus it is important to find the efficient data mining technique which can preserve privacy without causing high loss of information. This paper analyses various privacy preserving data mining techniques for their pros and cons and then gives a proposed concept to achieve privacy with minimum information loss.