Privacy Preserving Data Mining Methods Research Articles

Privacy-preserving data (stream) mining techniques and their impact on data mining accuracy: a systematic literature review

This study investigates existing input privacy-preserving data mining (PPDM) methods and privacy-preserving data stream mining methods (PPDSM), including their strengths and weaknesses. A further analysis was carried out to determine to what extent existing PPDM/PPDSM methods address the trade-off between data mining accuracy and data privacy which is a significant concern in the area. The systematic literature review was conducted using data extracted from 104 primary studies from 5 reputed databases. The scope of the study was defined using three research questions and adequate inclusion and exclusion criteria. According to the results of our study, we divided existing PPDM methods into four categories: perturbation, non-perturbation, secure multi-party computation, and combinations of PPDM methods. These methods have different strengths and weaknesses concerning the accuracy, privacy, time consumption, and more. Data stream mining must face additional challenges such as high volume, high speed, and computational complexity. The techniques proposed for PPDSM are less in number than the PPDM. We categorized PPDSM techniques into three categories (perturbation, non-perturbation, and other). Most PPDM methods can be applied to classification, followed by clustering and association rule mining. It was observed that numerous studies have identified and discussed the accuracy-privacy trade-off. However, there is a lack of studies providing solutions to the issue, especially in PPDSM.

Classification of Privacy Preserving Data Mining Algorithms: A Review

Nowadays, data from various sources are gathered and stored in databases. The collection of the data does not give a significant impact unless the database owner conducts certain data analysis such as using data mining techniques to the databases. Presently, the development of data mining techniques and algorithms provides significant benefits for the information extraction process in terms of the quality, accuracy, and precision results. Realizing the fact that performing data mining tasks using some available data mining algorithms may disclose sensitive information of data subject in the databases, an action to protect privacy should be taken into account by the data owner. Therefore, privacy preserving data mining (PPDM) is becoming an emerging field of study in the data mining research group. The main purpose of PPDM is to investigate the side effects of data mining methods that originate from the penetration into the privacy of individuals and organizations. In addition, it guarantees that the data miners cannot reveal any personal sensitive information contained in a database, while at the same time data utility of a sanitized database does not significantly differ from that of the original one. In this paper, we present a wide view of current PPDM techniques by classifying them based on their taxonomy techniques to differentiate the characteristics of each approach. The review of the PPDM methods is described comprehensively to provide a profound understanding of the methods along with advantages, challenges, and future development for researchers and practitioners.

Data mining privacy preserving: Research agenda

AbstractIn the modern days, the amount of the data and information is increasing along with their accessibility and availability, due to the Internet and social media. To be able to search this vast data set and to discover unknown useful data patterns and predictions, the data mining method is used. Data mining allows for unrelated data to be connected in a meaningful way, to analyze the data, and to represent the results in the form of useful data patterns and predictions that help and predict future behavior. The process of data mining can potentially violate sensitive and personal data. Individual privacy is under attack if some of the information leaks and reveals the identity of a person whose personal data were used in the data mining process. There are many privacy‐preserving data mining (PPDM) techniques and methods that have a task to preserve the privacy and sensitive data while providing accurate data mining results at the same time. PPDM techniques and methods incorporate different approaches that protect data in the process of data mining. The methodology that was used in this article is the systematic literature review and bibliometric analysis. This article identifieds the current trends, techniques, and methods that are being used in the privacy‐preserving data mining field to make a clear and concise classification of the PPDM methods and techniques with possibly identifying new methods and techniques that were not included in the previous classification, and to emphasize the future research directions.This article is categorized under: Commercial, Legal, and Ethical Issues > Security and Privacy

A Survey on Privacy-Preserving Data Mining Methods

In recent years, the increase in massive data in various fields has promoted the development of data mining. Still, the storage and mining of user data bring about the threat of privacy leakage. Researches on privacy-preserving data mining have become an increasingly significant research area. Focusing on the hidden dangers of privacy leakage in data mining methods, three leading privacy-preserving technologies came into being, which are data distortion technology, data encryption technology, and restricted publication technology. In this paper, we introduce and summarize the latest researches based on these privacy-preserving technologies. Besides, we describe the state-of-art research trends on privacy-preserving data mining methods in image processing and natural language processing.

Efficient Anonymization Algorithm for Multiple Sensitive Attributes

The data of medical applications over the internet contains sensitive data. There exist several methods that provide privacy for these data. Most of the privacy-preserving data mining methods make the assumption of the separation of quasi-identifiers (QID) from multiple sensitive attributes. But in reality, the attributes in a dataset possess both the features of QIDs and sensitive data. In this paper privacy model namely (vi…vj)-diversity is proposed. The proposed anonymization algorithm works for databases containing numerous sensitive QIDs. The real dataset is used for performance evaluation. Our system reduced the information loss for even huge number of attributes and the values of sensitive QID’s are protected.

An Active Genomic Data Recovery Attack

With the decreasing cost and availability of human genome sequencing, genomic privacy becomes an important issue. Several methods have been proposed in the literature to overcome these problems including cryptographic and privacy preserving data mining methods: homomorphic encryption, cryptographic hardware. In a recent work, Barman et. al studied privacy threats and practical solutions considering an SNP based scenario. The authors introduced a new protocol where a malicious medical center processes an active attack in order to retrieve genomic data of a given patient. The authors have mentioned that this protocol provides a trade-off between privacy and practicality. In this paper, we first give an overview of the system for SNP based risk calculation. We provide the definitions of privacy threats and briefly Barman et al.’s protocol and their solution. The authors proposed to use a weighted sum method of SNP coefficients for calculating disease tendency. They argue that the specific choice of the bases would prevent unique identification of SNPs. Our main observation is that this is not true. Contrary to the security claim, SNP combinations can be identified uniquely in many different scenarios. Our method exploits a pre-computed look-up table for retrieving SNPs’ values from the test result. An attacker can obtain all SNP values of a given patient by using the pre-computed look-up table. We provide practical examples of weights and pre-computed tables. We also mention that even in the case where the table is large and the attacker can not handle at one time, he can still gather information using multi queries. Our work shows that more realistic attack scenarios must be considered in the design of genetic security systems.

Privacy-Preserving Data Mining: Methods, Metrics, and Applications

The collection and analysis of data are continuously growing due to the pervasiveness of computing devices. The analysis of such information is fostering businesses and contributing beneficially to the society in many different fields. However, this storage and flow of possibly sensitive data poses serious privacy concerns. Methods that allow the knowledge extraction from data, while preserving privacy, are known as privacy-preserving data mining (PPDM) techniques. This paper surveys the most relevant PPDM techniques from the literature and the metrics used to evaluate such techniques and presents typical applications of PPDM methods in relevant fields. Furthermore, the current challenges and open issues in PPDM are discussed.

Privacy-Preserving Distributed Data Mining Techniques: A Survey

distributed data mining settings, leakage of the real data is not adequate because of privacy issues. To overcome this problem, numerous privacy-preserving distributed data mining practices have been suggested such as protect privacy of their data by perturbing it with a randomization algorithm and using cryptographic techniques. In this paper, we review and provide extensive survey on different privacy preserving data mining methods and analyses the representative techniques for privacy preserving data mining. We majorly discuss the distributed privacy preservation techniques which provide secure solutions using primitive operations of cryptographic protocols such as secure multi-party computation (SMPC), secret sharing schemes (SSS) and homomorphic encryption (HC). Keywordsmining, K-means clustering, Data privacy, Privacy preserving, Multiparty computation, Threshold Cryptography.

A comprehensive review on privacy preserving data mining

Preservation of privacy in data mining has emerged as an absolute prerequisite for exchanging confidential information in terms of data analysis, validation, and publishing. Ever-escalating internet phishing posed severe threat on widespread propagation of sensitive information over the web. Conversely, the dubious feelings and contentions mediated unwillingness of various information providers towards the reliability protection of data from disclosure often results utter rejection in data sharing or incorrect information sharing. This article provides a panoramic overview on new perspective and systematic interpretation of a list published literatures via their meticulous organization in subcategories. The fundamental notions of the existing privacy preserving data mining methods, their merits, and shortcomings are presented. The current privacy preserving data mining techniques are classified based on distortion, association rule, hide association rule, taxonomy, clustering, associative classification, outsourced data mining, distributed, and k-anonymity, where their notable advantages and disadvantages are emphasized. This careful scrutiny reveals the past development, present research challenges, future trends, the gaps and weaknesses. Further significant enhancements for more robust privacy protection and preservation are affirmed to be mandatory.

A New Algorithm-independent Method for Privacy-Preserving Classification Based on Sample Generation

With the development of data mining technologies, privacy protection is becoming a challenge for data mining applications in many fields. To solve this problem, many PPDM (privacy-preserving data mining) methods have been proposed. One important type of PPDM method is based on data perturbation. Only part of the data-perturbation-based methods is algorithm-irrelevant, which are favorable because common data mining algorithms can be used directly. This paper proposes a new algorithm-irrelevant PPDM method for classification based on sample generation. This method is a data-perturbation-based method and has three steps. First, it trains classifiers use the original data. Then, it generates new samples as the perturbed data randomly. Finally, it use the classifiers trained in the first step to predict these samples' category. The experiments show that this new method can produce usable data while protecting privacy well.

A fast perturbation algorithm using tree structure for privacy preserving utility mining

As one of the important approaches in privacy preserving data mining, privacy preserving utility mining has been studied to find more meaningful results while database privacy is ensured and to improve algorithm efficiency by integrating fundamental utility pattern mining and privacy preserving data mining methods. However, its previous approaches require a significant amount of time to protect the privacy of data holders because they conduct database scanning operations excessively many times until all important information is hidden. Moreover, as the size of a given database becomes larger and a user-specified minimum utility threshold becomes lower, their performance degradation may be so uncontrollable that they cannot operate normally. To solve this problem, we propose a fast perturbation algorithm based on a tree structure which more quickly performs database perturbation processes for preventing sensitive information from being exposed. We also present extensive experimental results between our proposed method and state-of-the-art algorithms using both real and synthetic datasets. They show the proposed method has not only outstanding privacy preservation performance that is comparable to the previous ones but also 5–10 times faster runtime than that of the existing approaches on average. In addition, the proposed algorithm guarantees better scalability than that of the latest ones with respect to databases with the characteristics of gradually increasing attributes and transactions.

Reversible privacy preserving data mining: a combination of difference expansion and privacy preserving

Privacy Preserving Data Mining (PPDM) can prevent private data from disclosure in data mining. However, the current PPDM methods damaged the values of original data where knowledge from the mined data cannot be verified from the original data. In this paper, we combine the concept and technique based on the reversible data hiding to propose the reversible privacy preserving data mining scheme in order to solve the irrecoverable problem of PPDM. In the proposed privacy difference expansion (PDE) method, the original data is perturbed and embedded with a fragile watermark to accomplish privacy preserving and data integrity of mined data and to also recover the original data. Experimental tests are performed on classification accuracy, probabilistic information loss, and privacy disclosure risk used to evaluate the efficiency of PDE for privacy preserving and knowledge verification.

Information and Data Privacy - An Indian Perspective

The concern of using personal information or data for intrusive and malicious purposes has given rise to the necessity for preserving and protecting the privacy of individuals and consumers mostly in developed countries. In developing countries like India the issue of privacy as it is related to individual consumers has not been of much importance. With the recent advances in the field of Data Mining the concerns regarding privacy have increased even in developing countries like India. In this paper we look at the existing privacy policies of some top Indian companies in the telecom, banking and insurance sectors. Privacy Preserving Data Mining (PPDM) is discussed and a framework to apply PPDM methods in chosen domains is suggested.

A Privacy-Preserving Data Mining Method Based on Singular Value Decomposition and Independent Component Analysis

Privacy protection is indispensable in data mining, and many privacy-preserving data mining (PPDM) methods have been proposed. One such method is based on singular value decomposition (SVD), which uses SVD to find unimportant information for data mining and removes it to protect privacy. Independent component analysis (ICA) is another data analysis method. If both SVD and ICA are used, unimportant information can be extracted more comprehensively. Accordingly, this paper proposes a new PPDM method using both SVD and ICA. Experiments show that our method performs better in preserving privacy than the SVD-based methods while also maintaining data utility.