Privacy Preserving Clustering on Horizontally Partitioned Data

Data mining has been a popular research area for more than a decade due to its vast spectrum of applications. The power of data mining tools to extract hidden information that cannot be otherwise seen by simple querying proved to be useful. However, the popularity and wide availability of data mining tools also raised concerns about the privacy of individuals. The aim of privacy preserving data mining researchers is to develop data mining techniques that could be applied on databases without violating the privacy of individuals. Privacy preserving techniques for various data mining models have been proposed, initially for classification on centralized data then for association rules in distributed environments. In this work, we propose methods for constructing the dissimilarity matrix of objects from different sites in a privacy preserving manner which can be used for privacy preserving clustering as well as database joins, record linkage and other operations that require pair-wise comparison of individual private data objects horizontally distributed to multiple sites

Secure Computation in Privacy Preserving Data Mining

Data mining is a process in which data collected from different sources is analyzed for useful information. Because data mini ng tools provides a base for upcoming trends and reactions by reading through databases for secret patterns, they allow organizations to make proactive, knowledge - driven actions and the problems that were previously too much time - consuming to resolve. Data mining software is one of a number of analytical tools for analyzing data. In the field of data mining the Privacy is most important issue when data is shared. A fruitful direction for future trends of data mining research will be the enhancement of methods that incorporate privacy concerns. Most of the methods use random permutation techniques to mask the data, for preserving the privacy of sensitive data. Randomize response techniques were dev eloped for the purpose of protecting surveys privacy and avoiding g biased answers. The proposed work is to enhance the privacy level in RR technique using four group schemes. First according to the algorithm random attributes a, b, c, d were considered, then the randomization hav e been performed on every dataset accordi ng to the values of theta. Then ID3 and CART algorithm are applied on the randomized data. The result shows that by increasing the group, the privacy level will increase. This work shows that as compared with three group scheme with four groups scheme the accuracy decreases 6% but the privacy increases 65

A Toolbox for privacy preserving distributed data mining

Distributed structure of individual data makes it necessary for data holders to perform collaborative analysis over the collective database for better data mining results. However each site has to ensure the privacy of its individual data, which means no information is revealed about individual values. Privacy preserving distributed data mining is utilized for that purpose. In this study, we try to draw more attention to the topic of privacy preserving data mining by showing a model which is realistic for data mining, and allows for very efficient protocols. We give two protocols which are useful tools in data mining: a protocol for Yaoѫs millionaires problem, and a protocol for numerical distance. Our solution to Yaoѫs millionaires problem is of independent interest since it gives a solution which improves on known protocols with respect to both computation complexity and communication overhead. This protocol can be used for different purposes in privacy preserving data mining algorithms such as comparison and equality test of data records. Our numerical distance protocol is also applicable to variety of algorithms. In this study we applied our numerical distance protocol in a privacy preserving distributed clustering protocol for horizontally partitioned data. We show application of our protocol over different attribute types such as interval-scaled,binary, nominal, ordinal, ratio-scaled, and alphanumeric. We present proof of security of our protocol, and explain communication, and computation complexity analysis indetail

DPWeka: Achieving Differential Privacy in WEKA

Organizations belonging to the government, commercial, and non-profit industries collect and store large amounts of sensitive data, which include medical, financial, and personal information. They use data mining methods to formulate business strategies that yield high long-term and short-term financial benefits. While analyzing such data, the private information of the individuals present in the data must be protected for moral and legal reasons. Current practices such as redacting sensitive attributes, releasing only the aggregate values, and query auditing do not provide sufficient protection against an adversary armed with auxiliary information. In the presence of additional background information, the privacy protection framework, differential privacy, provides mathematical guarantees against adversarial attacks. Existing platforms for differential privacy employ specific mechanisms for limited applications of data mining. Additionally, widely used data mining tools do not contain differentially private data mining algorithms. As a result, for analyzing sensitive data, the cognizance of differentially private methods is currently limited outside the research community. This thesis examines various mechanisms to realize differential privacy in practice and investigates methods to integrate them with a popular machine learning toolkit, WEKA. We present DPWeka, a package that provides differential privacy capabilities to WEKA, for practical data mining. DPWeka includes a suite of differential privacy preserving algorithms which support a variety of data mining tasks including attribute selection and regression analysis. It has provisions for users to control privacy and model parameters, such as privacy mechanism, privacy budget, and other algorithm specific variables. We evaluate private algorithms on real-world datasets, such as genetic data and census data, to demonstrate the practical applicability of DPWeka

PRIVACY PRESERVING DATA MINING ALGORITHMS

The problem of Privacy Preserving Data Mining (PPDM). It describes some of the common cryptographic tools and constructs used in several PPDM techniques. The paper describes an overview of some of the well known PPDM algorithms, - ID3 for decision tree, association rule mining, EM clustering, frequency mining and Naïve Bayes. Most of these algorithms are usually a modification of a well known data mining algorithm along with some privacy preserving techniques. The paper finally describes the problem of using a model without knowing the model rules on context of passenger classification at the airlines security checkpoint by homeland security. This paper is intended to be a summary and a high level overview of PPDM

The Role of Data Mining in Information Security

Security and Privacy protection have been a public policy concern for decades. However, rapid technological changes, the rapid growth of the internet and electronic commerce, and the development of more sophisticated methods of collecting, analyzing, and using personal information have made privacy a major public and government issues. The field of data mining is gaining significance recognition to the availability of large amounts of data, easily collected and stored via computer systems.  Recently, the large amount of data, gathered from various channels, contains much personal information. When personal and sensitive data are published and/or analyzed, one important question to take into account is whether the analysis violates the privacy of individuals whose data is referred to. The importance of information that can be used to increase revenue cuts costs or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data privacy is growing constantly. For this reason, many research works have focused on privacy-preserving data mining, proposing novel techniques that allow extracting knowledge while trying to protect the privacy of users. Some of these approaches aim at individual privacy while others aim at corporate privacy [1]. 

LAYERED APPROACH FOR PERSONALIZED SEARCH ENGINE LOGS PRIVACY PRESERVING

ABSTRACT In this paper we examine the problem of defending privacy for publishing search engine logs. Search engines play a vital role in the navigation through the enormity of the Web. Privacy-preserving data publishing (PPDP) provides techniques and tools for publishing helpful information while preserving data privacy. Recently, PPDP has received significant attention in research communities, and several approaches have been proposed for different data publishing situations. In this paper we learn privacy preservation for the publication of search engine query logs. Consider a subject that even after eliminating all personal characteristics of the searcher, which can serve as associations to his identity, the magazine of such data, is still subject to privacy attacks from opponents who have partial knowledge about the set. Our tentative results show that the query log can be appropriately anonymized against the particular attack, while retaining a significant volume of helpful data. In this paper we learn about problem in search logs and why the log is not secure and how to create log secure using data mining algorithm and methods like Generalization, Suppression and Quasi identifier

Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation

The growing expanse of e-commerce and the widespread availability of online databases raise many fears regarding loss of privacy and many statistical challenges. Even with encryption and other nominal forms of protection for individual databases, we still need to protect against the violation of privacy through linkages across multiple databases. These issues parallel those that have arisen and received some attention in the context of homeland security. Following the events of September 11, 2001, there has been heightened attention in the United States and elsewhere to the use of multiple government and private databases for the identification of possible perpetrators of future attacks, as well as an unprecedented expansion of federal government data mining activities, many involving databases containing personal information. We present an overview of some proposals that have surfaced for the search of multiple databases which supposedly do not compromise possible pledges of confidentiality to the individuals whose data are included. We also explore their link to the related literature on privacy-preserving data mining. In particular, we focus on the matching problem across databases and the concept of ``selective revelation'' and their confidentiality implications.Comment: Published at http://dx.doi.org/10.1214/088342306000000240 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org