Privacy-Preserving Collaborative Association Rule Mining

In recent times, the development of privacy technologies has promoted the speed of research on privacy-preserving collaborative data mining. People borrowed the ideas of secure multi-party computation and developed secure multi-party protocols to deal with privacy-preserving collaborative data mining problems. Random perturbation was also identified to be an efficient estimation technique to solve the problems. Both secure multi-party protocol and random perturbation technique have their advantages and shortcomings. In this paper, we develop a new approach that combines existing techniques in such a way that the new approach gains the advantages from both of them

Secure and Distributed Approach for Mining Association Rules

Data mining is the process of extracting trends from data sources. Domain exerts can make use of the trends to derive business intelligence. Big organizations store data in multiple server and often data is horizontally distributed. Mining such database provides useful and actionable knowledge which can help in making well informed decisions. However, secure mining of extracting association rules can provide interesting information that can help enterprises to make expert decisions. In this paper, we propose an algorithm and have a secure mechanism in order to mine association rules for deriving knowledge. We also incorporated auditing of data in the proposed system. We built a prototype application that demonstrates the secure mining of association rules with support and confidence. The statistical measures such as support and confidence help in knowing the usefulness of the rules. The empirical results are encouraging

Privacy Preserving Access of Outsourced Data in Heterogeneous Databases

- Privacy is main concern in the world, among present technological phase. Information security has become a dangerous issue since the information sharing has a common need. Recently, privacy issues have been increased enormously when internet is flourishing with forums, social media, blogs and e-commerce, etc. Hence research area is retaining privacy in data mining. The sensitive data of the data owners should not be known to the third parties and other data owners. To make it efficient, the horizontal partitioning is done on the heterogeneous databases is introduced to improve privacy and efficiency. we address the major issues of privacy preservation in information mining. In particular, we consider to provide protection between different data owners and to give privacy between them by partitioning the databases horizontally and the data2019;s are available in the heterogeneous databases. Our proposed work is to center around the study of security saving on unknown databases and conceiving private refresh methods to database frameworks that backings thoughts of obscurity assorted than k-secrecy. Symmetric homomorphic encryption scheme, which is significantly more efficient than the asymmetric schemes. Our proposed work helps the valid user can extract with key issue in partition data in automated approach and the data2019;s are partitioned horizontally

Data Mining Applications in Banking Sector While Preserving Customer Privacy

In real-life data mining applications, organizations cooperate by using each other’s data on the same data mining task for more accurate results, although they may have different security and privacy concerns. Privacy-preserving data mining (PPDM) practices involve rules and techniques that allow parties to collaborate on data mining applications while keeping their data private. The objective of this paper is to present a number of PPDM protocols and show how PPDM can be used in data mining applications in the banking sector. For this purpose, the paper discusses homomorphic cryptosystems and secure multiparty computing. Supported by experimental analysis, the paper demonstrates that data mining tasks such as clustering and Bayesian networks (association rules) that are commonly used in the banking sector can be efficiently and securely performed. This is the first study that combines PPDM protocols with applications for banking data mining. Doi: 10.28991/ESJ-2022-06-06-014 Full Text: PD

Mining of Data Association in Distributed Databases

Abstract Data mining is the process of extracting information from data set and transform it into an understandable structure for further processing. The goal is to extract pattern and knowledge from large amount of data. Data mining can be used to obtain more accurate prediction result. Among many data mining techniques association rules mining is most widely used

Modeling the Product Space as a Network

In the market basket setting, we are given a series of transactions each composed of one or more items and the goal is to find relationships between items, usually sets of items that tend to occur in the same transaction. Association rules, a popular approach for mining such data, are limited in the ability to express complex interactions between items. Our work defines some of these limitations and addresses them by modeling the set of transactions as a network. We develop both a general methodology for analyzing networks of products, and a privacy-preserving protocol such that product network information can be securely shared among stores. In general, our network based view of transactional data is able to infer relationships that are more expressive and expansive than those produced by a typical association rules analysis

Security in Data Mining- A Comprehensive Survey

Data mining techniques, while allowing the individuals to extract hidden knowledge on one hand, introduce a number of privacy threats on the other hand. In this paper, we study some of these issues along with a detailed discussion on the applications of various data mining techniques for providing security. An efficient classification technique when used properly, would allow an user to differentiate between a phishing website and a normal website, to classify the users as normal users and criminals based on their activities on Social networks (Crime Profiling) and to prevent users from executing malicious codes by labelling them as malicious. The most important applications of Data mining is the detection of intrusions, where different Data mining techniques can be applied to effectively detect an intrusion and report in real time so that necessary actions are taken to thwart the attempts of the intruder. Privacy Preservation, Outlier Detection, Anomaly Detection and PhishingWebsite Classification are discussed in this paper