The Study of Privacy Preserving Data Mining Technology for Information Security

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.

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.

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

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 in Data Mining for Health Care

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.

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.

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.

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.

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%.

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.

Data mining is the process of posing queries and extracting patterns, often previously unknown from large quantities of data using pattern matching or other reasoning techniques. Data mining has many applications in security including for national security as well as for cyber security. The threats to national security include attacking buildings, destroying critical infrastructures such as power grids and telecommunication systems. Data mining techniques are being investigated to find out who the suspicious people are and who is capable of carrying out terrorist activities. Cyber security is involved with protecting the computer and network systems against corruption due to Trojan horses, worms and viruses. Data mining is also being applied to provide solutions such as intrusion detection and auditing. The first part of the presentation will discuss my joint research with Prof. Latifur Khan and our students at the University of Texas at Dallas on data mining for cyber security applications For example; anomaly detection techniques could be used to detect unusual patterns and behaviors. Link analysis may be used to trace the viruses to the perpetrators. Classification may be used to group various cyber attacks and then use the profiles to detect an attack when it occurs. Prediction may be used to determine potential future attacks depending in a way on information learnt about terrorists through email and phone conversations. Data mining is also being applied for intrusion detection and auditing. Other applications include data mining for malicious code detection such as worm detection and managing firewall policies. This second part of the presentation will discuss the various types of threats to national security and describe data mining techniques for handling such threats. Threats include non real-time threats and real-time threats. We need to understand the types of threats and also gather good data to carry out mining and obtain useful results. The challenge is to reduce false positives and false negatives. The third part of the presentation will discuss some of the research challenges. We need some form of real-time data mining, that is, the results have to be generated in real-time, we also need to build models in real-time for realtime intrusion detection. Data mining is also being applied for credit card fraud detection and biometrics related applications. While some progress has been made on topics such as stream data mining, there is still a lot of work to be done here. Another challenge is to mine multimedia data including surveillance video. Finally, we need to maintain the privacy of individuals. Much research has been carried out on privacy preserving data mining. In summary, the presentation will provide an overview of data mining, the various types of threats and then discuss the applications of data mining for malicious code detection, cyber security and national security. Then we will discuss the consequences to privacy.

Data mining is the process of posing queries and extracting patterns, often previously unknown from large quantities of data using pattern matching or other reasoning techniques. Data mining has many applications in security including for national security as well as for cyber security. The threats to national security include attacking buildings, destroying critical infrastructures such as power grids and telecommunication systems. Data mining techniques are being investigated to find out who the suspicious people are and who is capable of carrying out terrorist activities. Cyber security is involved with protecting the computer and network systems against corruption due to Trojan horses, worms and viruses. Data mining is also being applied to provide solutions such as intrusion detection and auditing. The first part of the presentation will discuss my joint research with Prof. Latifur Khan and our students at the University of Texas at Dallas on data mining for cyber security applications For example; anomaly detection techniques could be used to detect unusual patterns and behaviors. Link analysis may be used to trace the viruses to the perpetrators. Classification may be used to group various cyber attacks and then use the profiles to detect an attack when it occurs. Prediction may be used to determine potential future attacks depending in a way on information learnt about terrorists through email and phone conversations. Data mining is also being applied for intrusion detection and auditing. Other applications include data mining for malicious code detection such as worm detection and managing firewall policies. This second part of the presentation will discuss the various types of threats to national security and describe data mining techniques for handling such threats. Threats include non real-time threats and real-time threats. We need to understand the types of threats and also gather good data to carry out mining and obtain useful results. The challenge is to reduce false positives and false negatives. The third part of the presentation will discuss some of the research challenges. We need some form of real-time data mining, that is, the results have to be generated in real-time, we also need to build models in real-time for realtime intrusion detection. Data mining is also being applied for credit card fraud detection and biometrics related applications. While some progress has been made on topics such as stream data mining, there is still a lot of work to be done here. Another challenge is to mine multimedia data including surveillance video. Finally, we need to maintain the privacy of individuals. Much research has been carried out on privacy preserving data mining. In summary, the presentation will provide an overview of data mining, the various types of threats and then discuss the applications of data mining for malicious code detection, cyber security and national security. Then we will discuss the consequences to privacy.