Modified Anonymity Model for Privacy Preserving Data Mining

Huge volume of data from domain specific applications such as medical, financial, telephone, shopping records and individuals are regularly generated. Sharing of these data is proved to be beneficial for data mining application. Since data mining often involves data that contains personally identifiable information and therefore releasing such data may result in privacy breaches. On one hand such data is an important asset to business decision making by analyzing it. On the other hand data privacy concerns may prevent data owners from sharing information for data analysis. In order to share data while preserving privacy, data owner must come up with a solution which achieves the dual goal of privacy preservation as well as accuracy of data mining task mainly clustering and classification. Privacy Preserving Data Publishing (PPDP) is a study of eliminating privacy threats like linkage attack while preserving data utility by anonymizing data set before publishing. Proposed work is an ...

In recent years, privacy-preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. A number of algorithmic techniques have been designed for privacy-preserving data mining. In this paper, we provide a review of the state-of-the-art methods for privacy. We discuss methods for randomization, k-anonymization, and distributed privacy-preserving data mining. We also discuss cases in which the output of data mining applications needs to be sanitized for privacy-preservation purposes. We discuss the computational and theoretical limits associated with privacy-preservation over high dimensional data sets.

International Journal of Science Technology & Engineering

Data mining is the process of extracting interesting patterns or knowledge from huge amount of data. In recent years, there has been a tremendous growth in the amount of personal data that can be collected and analyzed by the organizations. As hardware costs go down, organizations find it easier than ever to keep any piece of information acquired from the ongoing activities of their clients. These organizations constantly seek to make better use of the data they possess, and utilize data mining tools to extract useful knowledge and patterns from the data. Also, the current trend in business collaboration shares the data and mine results to gain mutual benefit [2]. This data does not include explicit identifiers of an individual like name or address but it does contain data like date of birth, pin code, sex, marital-status etc. which when combined with other publicly released data like voter registration data can identify an individual. The previous literature of privacy preserving data publication has focused on performing “one-time” releases. Specifically, none of the existing solutions supports re-publication of the micro data multiple time publishing, after it has been updated with insertions and deletions. This is a serious drawback, because currently a publisher cannot provide researchers with the most recent dataset continuously. Based on survey of theoretical analysis, we develop a new generalization principle l-scarcity that effectively limits the risk of privacy disclosure in re-publication. And it’s a new method modifying of l-diversity and m-invariance by combining of these two methods. They provide a privacy on re-publication of the microdata. We consider a more realistic setting of sequential releases by Insertions, deletions and updates and Transient/permanent values. We cannot simply adapt these existing privacy models to this realistic setting.

— Privacy preserving data mining has become increasingly popular because it allows sharing of private sensitive data for analysis purposes. The concept of privacy preserving data mining has been proposed in response to these privacy concerns. The main goal of this research work has introduced a new k-Anonymity algorithm which is capable of transforming a non anonymous data set into a k-Anonymity data set. K-Anonymity model is thus to transform a table so that no one can make high-probability associations between records in the table and the corresponding entities. In order to achieve this goal, the K-Anonymity model requires that any record in a table be indistinguishable from at least (k−1) other records with respect to the predetermined quasi-identifier. Finally the modified dataset is used for clustering.

2013

Data mining is the extraction of vast interesting patterns or knowledge from huge amount of data. The initial idea of privacy-preserving data mining PPDM was to extend traditional data mining techniques to work with the data modified to mask sensitive information. The key issues were how to modify the data and how to recover the data mining result from the modified data. Privacy-preserving data mining considers the problem of running data mining algorithms on confidential data that is not supposed to be revealed even to the party running the algorithm. In contrast, privacy-preserving data publishing (PPDP) may not necessarily be tied to a specific data mining task, and the data mining task may be unknown at the time of data publishing. PPDP studies how to transform raw data into a version that is immunized against privacy attacks but that still supports effective data mining tasks. Privacy-preserving for both data mining (PPDM) and data publishing (PPDP) has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. One well studied approach is the k-anonymity model [1] which in turn led to other models such as confidence bounding, l-diversity, t-closeness, (α,k)-anonymity, etc. In particular, all known mechanisms try to minimize information loss and such an attempt provides a loophole for attacks. The aim of this paper is to present a survey for most of the common attacks techniques for anonymization-based PPDM & PPDP and explain their effects on Data Privacy.

Sharing, transferring, mining and publishing data are fundamental operations in day to day life. Preserving the privacy of individuals is essential one. Sensitive personal information must be protected when data are published. There are two kinds of risks namely attributing disclosure and identity disclosure that affects privacy of individuals whose data are published. Early Researchers have contributed new methods namely k-anonymity, l-diversity, t-closeness to preserve privacy. K-anonymity method preserves privacy of individuals against identity disclosure attack alone. But Attribute disclosure attack makes compromise this method. Limitation of k-anonymity is fulfilled through l-diversity method. But it does not satisfy the privacy against identity disclosure attack and attribute disclosure attack in some scenarios. The efficiency of t-closeness method is better than k-anonymity and l-diversity. But the complexity of Computation is more than other proposed methods. The k-anonymity method use for preserving the privacy of individuals’ sensitive information from attribute and identity disclosure attacks [1].

knowledge from huge amount of data. In recent years, there has been a tremendous growth in the amount of personal data that can be collected and analyzed by the organizations. Organizations such as credit card companies, real estate companies and hospitals collect and hold large volumes of data for their research purposes. E.g. National Institute of health. When these organizations publish data containing a lot of sensitive information. The importance of sharing data for research and knowledge discovery has been well-recognized. However, sharing data that contains sensitive personal information, such as insurance data, medical record, etc across organization boundaries can raise serious privacy concerns. There is a need to preserve the privacy of the individuals in data set . K-anonymity is one of the easy and efficient techniques to achieve privacy in many data publishing applications. In k-anonymity, all tuples of releasing database are generalized to make it anonymize which lead to data utility reduction and more information loss of publishing table. Sensitive attribute based anonymity method is very useful in preserving the privacy of individuals in organization’s publication of data. It reduces information loss to the researchers by providing sensitive levels. This method also avoids Homogeneity attack and Background attacks.

In now days the information sharing is very important. One organization shares the information of user to another organization for the better survey purpose. But the sensitive data of user will not be disclosed. So for that purpose we have to hide some sensitive data of user for that the data must be encrypted. K-anonymity algorithm is one of the ways to encrypt data so that data cannot be stealing and the information in the data will not modify. But there is some way to attack on the k-anonymity encrypted data. One of the way is background knowledge attack, in this if the attacker knows some basic information about the use then he can get the detail from database. If we can add some more data in the original database and the apply k-anonymity algorithm so that the attacker is get more rows of data and he will confuse so the data should be protected from the attacker.

… International Workshop on Database …, 2009

Privacy preserving data mining has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes .So people have become increasingly unwilling to share their data, frequently resulting in individuals either refusing to share their data or providing incorrect data. In recent years, privacy preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. We discuss method for randomization, kanonymization, and distributed privacy preserving data mining. Knowledge is supremacy and the more knowledgeable we are about information break-in, we are less prone to fall prey to the evil hacker sharks of information technology. In this paper, we provide a review of the state-of-the-art methods for privacy and analyze the representative technique for privacy preserving data mining and points out their merits and demerits. Finally the present problems and directions for future research are discussed.

In organization large amount of data are collected daily and these data are used by the organization for data mining tasks. These data collected may contain sensitive attribute which not disclosed by un-trusted user. Privacy is very important when release the data for sharing purpose or mining. Privacy preserving data mining allow publishing data while same time it protect the sensitive or private data. For privacy preserving there are many technique like k-anonymity, cryptography, blocking based, data Perturbation etc. In this paper, various privacy preserving approaches in data sharing and their merits and demerits are analyzed.