A Review on: Association Rule Mining Using Privacy for Partitioned Database

Data Analysis techniques that are Association manage mining and Frequent thing set mining are two prominent and broadly utilized for different applications. The conventional framework concentrated independently on vertically parceled database and on a level plane apportioned databases on the premise of this presenting a framework which concentrate on both on a level plane and vertically divided databases cooperatively with protection safeguarding component. Information proprietors need to know the continuous thing sets or affiliation rules from an aggregate information set and unveil or uncover as few data about their crude information as could reasonably be expected to other information proprietors and outsiders. To guarantee information protection a Symmetric Encryption Technique is utilized to show signs of improvement result. Cloud supported successive thing set mining arrangement used to exhibit an affiliation govern mining arrangement. The subsequent arrangements are intended for outsourced databases that permit various information proprietors to proficiently share their information safely without trading off on information protection. Information security is one of the key procedures in outsourcing information to different outside clients. Customarily Fast Distribution Mining calculation was proposed for securing conveyed information. These business locales an issue by secure affiliation governs over parceled information in both even and vertical. A Frequent thing sets calculation and Distributed affiliation administer digging calculation is used for doing above method adequately in divided information, which incorporates administrations of the information in outsourcing process for disseminated databases. This work keeps up or keeps up proficient security over vertical and flat perspective of representation in secure mining applications

Literature Review on Secure Mining of Association Rules in Horizontally Distributed Databases

Data and knowledge Engineering is one of the area under data mining. Which can extract important knowledge from large database, but sometimes these database are divided among various parties. This paper addresses a fast distributed mining of association rules over horizontally distributed data. This paper presents different methods for secure mining of association rules in horizontally distributed databases. The main aim of this paper is protocol for secure mining of association rules in horizontally distributed databases. The current main protocol is that of Kantarcioglu and Clifton. This protocol, like theirs, is based on the Fast Distributed Mining (FDM) algorithm of Cheung et al., which is an unsecured distributed version of the Apriori algorithm. The main components in this protocol are two novel secure multi-party algorithms — one that computes the union of private subsets that each of the interacting players hold, and another that tests the inclusion of an element held by one player in a subset held by another. This protocol offers improved privacy with respect to the protocol in. In addition, it is simpler and is significantly more efficient in terms of communication rounds, communication cost and computational cost

Privacy by Design in Data Mining

Privacy is ever-growing concern in our society: the lack of reliable privacy safeguards in many current services and devices is the basis of a diffusion that is often more limited than expected. Moreover, people feel reluctant to provide true personal data, unless it is absolutely necessary. Thus, privacy is becoming a fundamental aspect to take into account when one wants to use, publish and analyze data involving sensitive information. Many recent research works have focused on the study of privacy protection: some of these studies aim at individual privacy, i.e., the protection of sensitive individual data, while others aim at corporate privacy, i.e., the protection of strategic information at organization level. Unfortunately, it is in- creasingly hard to transform the data in a way that it protects sensitive information: we live in the era of big data characterized by unprecedented opportunities to sense, store and analyze complex data which describes human activities in great detail and resolution. As a result anonymization simply cannot be accomplished by de-identification. In the last few years, several techniques for creating anonymous or obfuscated versions of data sets have been proposed, which essentially aim to find an acceptable trade-off between data privacy on the one hand and data utility on the other. So far, the common result obtained is that no general method exists which is capable of both dealing with “generic personal data” and preserving “generic analytical results”. In this thesis we propose the design of technological frameworks to counter the threats of undesirable, unlawful effects of privacy violation, without obstructing the knowledge discovery opportunities of data mining technologies. Our main idea is to inscribe privacy protection into the knowledge discovery technol- ogy by design, so that the analysis incorporates the relevant privacy requirements from the start. Therefore, we propose the privacy-by-design paradigm that sheds a new light on the study of privacy protection: once specific assumptions are made about the sensitive data and the target mining queries that are to be answered with the data, it is conceivable to design a framework to: a) transform the source data into an anonymous version with a quantifiable privacy guarantee, and b) guarantee that the target mining queries can be answered correctly using the transformed data instead of the original ones. This thesis investigates on two new research issues which arise in modern Data Mining and Data Privacy: individual privacy protection in data publishing while preserving specific data mining analysis, and corporate privacy protection in data mining outsourcing

Synthetic dataset generation with itemset-based generative models

This paper proposes three different data generators, tailored to transactional datasets, based on existing itemset-based generative models. All these generators are intuitive and easy to implement and show satisfactory performance. The quality of each generator is assessed by means of three different methods that capture how well the original dataset structure is preserved.Both authors have been partially supported by TIN2017-89244-R from MINECO (Spain’s Ministerio de Economia, Industria y Competitividad) and the recognition 2017SGR-856 (MACDA) from AGAUR (Generalitat de Catalunya). Christian Lezcano is supported by Paraguay’s Foreign Postgraduate Scholarship Programme Don Carlos Antonio López (BECAL).Peer ReviewedPostprint (author's final draft

Synthetic Dataset Generation with Itemset-Based Generative Models

This paper proposes three different data generators, tailored to transactional datasets, based on existing itemset-based generative models. All these generators are intuitive and easy to implement and show satisfactory performance. The quality of each generator is assessed by means of three different methods that capture how well the original dataset structure is preserved.Comment: IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW@RDSA 2019), Oct 201

Exploring the Existing and Unknown Side Effects of Privacy Preserving Data Mining Algorithms

The data mining sanitization process involves converting the data by masking the sensitive data and then releasing it to public domain. During the sanitization process, side effects such as hiding failure, missing cost and artificial cost of the data were observed. Privacy Preserving Data Mining (PPDM) algorithms were developed for the sanitization process to overcome information loss and yet maintain data integrity. While these PPDM algorithms did provide benefits for privacy preservation, they also made sure to solve the side effects that occurred during the sanitization process. Many PPDM algorithms were developed to reduce these side effects. There are several PPDM algorithms created based on different PPDM techniques. However, previous studies have not explored or justified why non-traditional side effects were not given much importance. This study reported the findings of the side effects for the PPDM algorithms in a newly created web repository. The research methodology adopted for this study was Design Science Research (DSR). This research was conducted in four phases, which were as follows. The first phase addressed the characteristics, similarities, differences, and relationships of existing side effects. The next phase found the characteristics of non-traditional side effects. The third phase used the Privacy Preservation and Security Framework (PPSF) tool to test if non-traditional side effects occur in PPDM algorithms. This phase also attempted to find additional unknown side effects which have not been found in prior studies. PPDM algorithms considered were Greedy, POS2DT, SIF_IDF, cpGA2DT, pGA2DT, sGA2DT. PPDM techniques associated were anonymization, perturbation, randomization, condensation, heuristic, reconstruction, and cryptography. The final phase involved creating a new online web repository to report all the side effects found for the PPDM algorithms. A Web repository was created using full stack web development. AngularJS, Spring, Spring Boot and Hibernate frameworks were used to build the web application. The results of the study implied various PPDM algorithms and their side effects. Additionally, the relationship and impact that hiding failure, missing cost, and artificial cost have on each other was also understood. Interestingly, the side effects and their relationship with the type of data (sensitive or non-sensitive or new) was observed. As the web repository acts as a quick reference domain for PPDM algorithms. Developing, improving, inventing, and reporting PPDM algorithms is necessary. This study will influence researchers or organizations to report, use, reuse, or develop better PPDM algorithms