DISTORTION-BASED HEURISTIC METHOD FOR SENSITIVE ASSOCIATION RULE HIDING

In the past few years, privacy issues in data mining have received considerable attention in the data mining literature. However, the problem of data security cannot simply be solved by restricting data collection or against unauthorized access, it should be dealt with by providing solutions that  not only protect sensitive information, but also not affect to the accuracy of the results in data mining and not violate the sensitive knowledge related with individual privacy or competitive advantage in businesses. Sensitive association rule hiding is an important issue in privacy preserving data mining. The aim of association rule hiding is to minimize the side effects on the sanitized database, which means to reduce the number of missing non-sensitive rules and the number of generated ghost rules. Current methods for hiding sensitive rules cause side effects and data loss. In this paper, we introduce a new distortion-based method to hide sensitive rules. This method proposes the determination of critical transactions based on the number of non-sensitive maximal frequent itemsets that contain at least one item to the consequent of the sensitive rule, they can be directly affected by the modified transactions. Using this set, the number of non-sensitive itemsets that need to be considered is reduced dramatically. We compute the smallest number of transactions for modification in advance to minimize the damage to the database. Comparative experimental results on real datasets showed that the proposed method can achieve better results than other methods with fewer side effects and data loss

Paillier based Privacy-Preserving Mining of Association Rules from Outsourced Transaction Databases

The Cloud computing is computing in which massive assembling of remote servers are managed to authorized centralized data storage and online access to computer resources , while Privacy-preserving data mining (PPDM) is one of the latest inclination in privacy and security studies. It is determined by one of the important positioning issues of the information era - the right to privacy. With the use of cloud computing services, an organization lack in computational resources can deploy its mining requires to an outsider service provider. However, both the elements and the association rules of the deployed database are observed as private property of the organization. The data owner converts its data and sends it to the server, ships mining queries to the server, and recoup the actual design from the extricate designs received from the outsider server for corporate privacy prevention. In this theory, we study the problems of outsourcing the association rule mining mechanisms within a corporate privacy-preserving framework. The Rob Frugal method is founded with defeat the security obligations of outsourced data. This method is an encryption plan which is based on one to one substitution ciphers for items and fake pattern from the database. In this system attacker discovers data by guessing attack, also man in the middle attack which is possible on Rob Frugal encryption to conquer this problem, the proposed technique encompasses Paillier encryption for enhancing the security level for outsourced data with the less complexity and to protect against the forging the contents of the correspondence. FP-growth algorithm is used for generating association rules for improving the performance and for preserving a homomorphic encryption algorithm Paillier cryptosystem is being used

Investigations in Privacy Preserving Data Mining

Data Mining, Data Sharing and Privacy-Preserving are fast emerging as a field of the high level of the research study. A close review of the research based on Privacy Preserving Data Mining revealed the twin fold problems, first is the protection of private data (Data Hiding in Database) and second is the protection of sensitive rules (Knowledge) ingrained in data (Knowledge Hiding in the database). The first problem has its impetus on how to obtain accurate results even when private data is concealed. The second issue focuses on how to protect sensitive association rule contained in the database from being discovered, while non-sensitive association rules can still be mined with traditional data mining projects. Undoubtedly, performance is a major concern with knowledge hiding techniques. This paper focuses on the description of approaches for Knowledge Hiding in the database as well as discuss issues and challenges about the development of an integrated solution for Data Hiding in Database and Knowledge Hiding in Database. This study also highlights directions for the future studies so that suggestive pragmatic measures can be incorporated in ongoing research process on hiding sensitive association rules

A Framework for High-Accuracy Privacy-Preserving Mining

To preserve client privacy in the data mining process, a variety of techniques based on random perturbation of data records have been proposed recently. In this paper, we present a generalized matrix-theoretic model of random perturbation, which facilitates a systematic approach to the design of perturbation mechanisms for privacy-preserving mining. Specifically, we demonstrate that (a) the prior techniques differ only in their settings for the model parameters, and (b) through appropriate choice of parameter settings, we can derive new perturbation techniques that provide highly accurate mining results even under strict privacy guarantees. We also propose a novel perturbation mechanism wherein the model parameters are themselves characterized as random variables, and demonstrate that this feature provides significant improvements in privacy at a very marginal cost in accuracy. While our model is valid for random-perturbation-based privacy-preserving mining in general, we specifically evaluate its utility here with regard to frequent-itemset mining on a variety of real datasets. The experimental results indicate that our mechanisms incur substantially lower identity and support errors as compared to the prior techniques

Semi-Trusted Mixer Based Privacy Preserving Distributed Data Mining for Resource Constrained Devices

In this paper a homomorphic privacy preserving association rule mining algorithm is proposed which can be deployed in resource constrained devices (RCD). Privacy preserved exchange of counts of itemsets among distributed mining sites is a vital part in association rule mining process. Existing cryptography based privacy preserving solutions consume lot of computation due to complex mathematical equations involved. Therefore less computation involved privacy solutions are extremely necessary to deploy mining applications in RCD. In this algorithm, a semi-trusted mixer is used to unify the counts of itemsets encrypted by all mining sites without revealing individual values. The proposed algorithm is built on with a well known communication efficient association rule mining algorithm named count distribution (CD). Security proofs along with performance analysis and comparison show the well acceptability and effectiveness of the proposed algorithm. Efficient and straightforward privacy model and satisfactory performance of the protocol promote itself among one of the initiatives in deploying data mining application in RCD.Comment: IEEE Publication format, International Journal of Computer Science and Information Security, IJCSIS, Vol. 8 No. 1, April 2010, USA. ISSN 1947 5500, http://sites.google.com/site/ijcsis

William H. Sorrell, Attorney General of Vermont, et al. v. IMS Health Inc., et al. - Amicus Brief in Support of Petitioners

On April 26, 2011, the US Supreme Court will hear oral arguments in the Vermont data mining case, Sorrell v. IMS Health Inc. Respondents claim this is the most important commercial speech case in a decade. Petitioner (the State of Vermont) argues this is the most important medical privacy case since Whalen v. Roe. The is an amicus brief supporting Vermont, written by law professors and submitted on behalf of the New England Journal of Medicin

Secret charing vs. encryption-based techniques for privacy preserving data mining

Privacy preserving querying and data publishing has been studied in the context of statistical databases and statistical disclosure control. Recently, large-scale data collection and integration efforts increased privacy concerns which motivated data mining researchers to investigate privacy implications of data mining and how data mining can be performed without violating privacy. In this paper, we first provide an overview of privacy preserving data mining focusing on distributed data sources, then we compare two technologies used in privacy preserving data mining. The first technology is encryption based, and it is used in earlier approaches. The second technology is secret-sharing which is recently being considered as a more efficient approach