The Time-consuming Task of Preparing a Data Set for Analysis in Data Mining

In the past decade, a number of PPDM techniques have been proposed to facilitate users in performing data mining tasks in privacy-sensitive environments. Agrawal and Srikant [3], as well as Lindell and Pinkas [63], were the first to introduce the notion of privacy-preserving under data mining applications. Existing PPDM techniques can be classified into two broad categories: data perturbation and data distribution. Data Perturbation Methods: With these methods, values of individual data records are perturbed by adding random noise in such a way that the distribution of the perturbed data look very deferent from that of the actual data. After such a transformation, the perturbed data is sent to the Miner to perform the desired data mining tasks. Agrawal and Srikant [3] proposed the first data perturbation technique that could be used to build a decision-tree classifier. A number of randomization-based methods were later proposed [6, 33, 34, 73, 104]. Data perturbation techniques are not, however, applicable to semantically- secure encrypted data. They also fail to produce accurate data mining results due to the addition of statistical noises to the data. Data Distribution Methods: These methods assume that the dataset is partitioned eitherhorizontallyorverticallyanddistributedacrossdifferentparties. The parties

Today we are doing a project report on Costco. For Sol and Robert Price in 1976 they asked friends and family to help out with an opening price of two point five millon to open Price Club on July twelfth, they open their shop in an air hanger on Boulevard in San Diego, California. They were originally going to serve only small business. Mr. Price found out that it will be more beneficial to serve select customers. Costco was founded by James Sinegal and Jeffery H. Brotman. Costco opened its doors in 1983 in Seattle, Washington. Price Club and Costco later merged and

Data mining can cause more problems than it worth. It can be a useful marketing tool for businesses, but at the risk of major privacy threats. If the results can include security issues, false information, and cause inefficiencies on both ends, then it needs to be considered and improved. With the proposed solutions, data mining can become more secure, a less of a misguided problem. The solutions are extremely reasonable, it is just a matter of the government and data mining companies putting in effort to make them

Data mining is the process of discovering and interpreting meaningful, previously hidden patterns in data. Extensively used in financial services, customer relationship management and retail, data mining helps alleviate data overload, by extracting value from volume. Furthermore, the focus of data mining is on the process, not a particular technique, used to make reasonably accurate predictions. “The recent emergence of data mining technology to analyze vast amounts of data opens new threats to information privacy”. (Brankovic & Estivill-Castro 1999, Clifton & Marks 1996, Estivill-Castro, Brankovik & Dowe 1999) There are many pieces of academia some that will be identified in this paper that identify such privacy risks and the ethical and legal ramifications that acquiesce

With the increased and widespread use of technologies, interest in data mining has increased rapidly. Companies are now utilized data mining techniques to exam their database looking for trends, relationships, and outcomes to enhance their overall operations and discover new patterns that may allow them to better serve their customers. Data mining provides numerous benefits to businesses, government, society as well as individual persons. However, like many technologies, there are negative things that caused by data mining such as invasion of privacy right. This paper tries to explore the advantages as well as the disadvantages of data mining. In addition, the ethical and global issues regarding the use of data mining

In recent years, privacy preserving data mining has become a hotspot in data mining. Today, the field of privacy has seen rapid advances in recent years because of the increase in the ability to store data. In particular, recent advances in the data mining field have led to increasing concerns about privacy. However, with the development of technologies, the emerging applications result in an accumulation of abundant personal privacy information, which will easily lead to the violence of personal privacy. Therefore, it is significant to study the privacy preserving data mining methods of new applications. Data mining is the process of extraction of data from a large database. Knowledge Discovery in Database (KDD) is another name of data mining. One of the most important topics in a research community is Privacy Preserving Data Mining (PPDM). It refers to the area of data mining that seeks to safeguard sensitive information from unsolicited or unsanctioned disclosure. The Success of Privacy Preserving data mining algorithms is measured in terms of its performance, data utility, level of uncertainty or resistance to data mining algorithms. The objective of privacy preserving data mining (PPDM) is to find a way to manipulate the dataset, so that the sensitive message can’t be disclosed in data mining.

little or no control over the data gathering process, with data often being collected for some

Abstract—Fast Distributed Mining (FDM) which generates a small number of candidate set and substantially reduce the number of messages to be passed at mining association rules. Distributed data mining offers a way by data can be shared without compromising privacy. The paper present secure protocols for the task of top-k subgroup discovery on horizontally partitioned data. In this setting, all sites use the same set of attributes and the quality of every subgroup depends on all databases. The approach finds patterns in the union of the databases, without disclosing the local databases. This is the first secure approach that tackles any of the supervised descriptive rule discovery tasks. It is simpler and significantly more efficient in terms of communication rounds, communication cost and computational cost.

Data is often far from perfect. Most of the data mining technique can tolerate and bare some level of imperfection and inconsistency in the data. This imperfection and inconsistency may cause loss of individual’s private data. Preserving privacy data mining focuses on obtaining positive result analysis. The data quality issues that often need to be addressed includes the presence of noise and outliers, missing inconsistent or duplicate data, biased and unrepresentative phenomenon and that the data is supposed to be. For preprocessing and pattern recognition it is mandatory to build the data in representable form before providing it to data mining techniques. In this paper we introduce Noise cure framework as a bridge between data preprocessing and data mining for preprocessing the raw data to bring in a form suitable for pattern recognition in preserving privacy data mining

Data mining is one of the most vital phase in rough set approach. Data mining is a technique for exploring data identical large size big data in orderly forms between variables and then to authorize the results by relating the perceived forms to new subsets of data.

In this paper, we propose a secure way of data sharing scheme, which gives secure key distribution and data sharing for dynamic group. The main contributions of our scheme

Currently, one of the major concerns is privacy to prevent the private data which they don’t want to share. This paper targeted on the previous literatures in the domain of Privacy Preserving Data Mining. From this analysis, it has been found that there is no only one method which is constant in all fields. All techniques operate in a separate way based on the pattern

A Bottled Water survey done by Mintel, in spring of 2015, found mid-priced brands show the highest market penetration. 68% of bottled water drinkers surveyed by Mintel say that they usually drink mid-priced brands. More than half of those surveyed said they usually drink value brands, showing a definite consumer preference towards mid-and value-priced brands on the market. Only 13% of those surveyed drank Premium water. (Mintel Group Ltd. 2015)

Due to the rapid growth in the use of Internet and its connected tools, an enormous amount of data are being produced on a daily basis. The concept of big data arrives when we were unable to manage this huge data with traditional methods. Big data is a mechanism of capturing, storing and analyzing the big datasets and also an idea of extracting some value from it. It is very handful while determining the root causes of failures, issues and defects in near-real time, creating coupons and other sales offers according to the customers shopping patterns, detecting any suspicious and fraudulent activities in real-time. As it is very advantageous, it also has some issues. Some of the common issues can be characterized into heterogeneity, complexity, timeless, scalability and privacy. The most important and significant challenge in the big data is to preserve privacy information of the customers, employees, and the organizations. It is very sensitive and includes conceptual, technical as well as legal significance.

Data Warehousing and Data Mining has always been associated with manufacturing companies, where sales and profit is the main driving force. Subsequently Higher Education has grown throughout the years; this growth is predominately associated with the increase of online institutions. This growth has resulted in higher education to adapt to a more business like institution (Lazerson, 2000).