Secure Mining Of Association Rule With Subgroup Discovery Essay

In “Privacy under Attack,” the authors discuss some potential ways of how our privacy could be in danger or stolen by companies or accessible by administration and corporations. According to the authors, some ways that our privacy could be in danger are first through video records and picture taken by traffic cameras and surveillance cameras for us. Second through illegal “wiretaps” by the government that could hear our conversations. Third, our privacy and personal data could be “monitored by corporations through the role of club cards, raffles, or refunds that outside companies’ collection of data about us can then be sold without noticing, given consumers the optional to search for the box on any frame to indicate they don’t want their personal

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

Definition 10: Utility-list.: [6] The utility-list of an itemset X in a database D is a set of tuples such that there is a tuple (tid; iutil; rutil) for each transaction Ttid containing X. The iutil is the utility of X in Ttid. i.e., u(X; Ttid). The rutil is remaining utility of element X in that transaction. Two known properties of HUIs are used in this algorithm[6].

Data mining: find for more hidden relationship through without any ideas about the existence of those relationship and data that they do not collect it before.

This is similar to apriori join step. In ith level combined items generates all possible ith level transaction for Ck (candidate itemsets) that using Lk-1 itemsets. Then frequency counts are discovered for each combination and generate linked list structure and allocate items in structure.

The protection of personalized data has been a major concern for insurers across the United States for many years. This concern has continued to grow due to an increase in the number of data breaches across all industries regarding medical health information. The passage of federal laws such as the Health Insurance Portability and Accountability Act as well as the passage of a variety of state legislation related to privacy breaches has changed the way in which firms deal with these issues, (Gatzlaff & McCullough, 2012). During this research, there was a collection of data that connected to the instances of HIPAA violations within the United States. There are various cases that have been reported through patients and employees where very personal medical information has been exposed unlawfully for personal gain. These cases have not only put a company at reputational risk. But these cases can also place a patient and or healthcare company in a terrible financial stipulation. This thesis will include a series of charts and tables that describe the fluctuation of such cases involving different examples of HIPAA violations. Not only will there be data of these instances but there will be illustrations of how both patients and healthcare employees exemplify HIPAA violations. These cases will be verified from an external and internal evaluation. Suggestive protocol will be demonstrated to guide one along to ensure the possibility of another case of HIPAA violation is prevented.

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

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

With advances in technology constantly happening, it can be hard to keep up with all of the latest trends. If organizations cannot keep up with the latest trends, it can lead to flaws in their security. Any flaws in security can have a detrimental effect on an organization’s database. Almost every organization has some sort of database, whether it is for maintaining customers, inventory, or vital information.

Data Mining is an analytic process designed to explore data (usually large amounts of data - typically business or market related - also known as "big data") in search of consistent patterns and systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. In recent years, with the tremendous development in the internet, data storage and processing technologies, privacy and security has become our major concerns in the field of data mining. Privacy preservation is one of the most important and challenging factor as the sensitive data should not be embarrassed by the adversery.this paper presents wide survey of different privacy preserving techniques and algorithms for privacy preserving data mining and points out the merits and demerits.

1 INTRODUCTION Public-key encryption is a powerful mechanism for ensuring the confidentiality of stored and transmitted data. The traditional encryption is regarded as a method to share data to some targeted person or device that the sender knows. This means the sender knows specifically which user he wants to share with. However, in many systems the message sender will want to share date with a group of people whose credentials or attributes accord to some access policy set by the sender.[8][9]A new vision of encryption[10]was presented, where the sender can express how he wants to share data in the encryption algorithm itself. The sender will give an attribute formula to describe the users who can decrypt this data.

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.

Abstract— Frequent subgraph mining (FSM) is a crucial task for explorative information analysis on graph information. Over the years, several algorithms are planned to unravel this task. These algorithms assume that the information structure of the mining task is tiny enough to suit within the main memory of a laptop. However, because the real-world graph information grows, each in size and amount, such Associate in Nursing assumption doesn 't hold any more. to beat this, some graph database-centric strategies are planned in recent years for finding FSM; but, a distributed resolution victimization MapReduce paradigm has not been explored extensively. Since, MapReduce is changing into the defacto paradigm for computation on large information, Associate in Nursing economical FSM algorithmic rule on this paradigm is of big demand. during this work, we have a tendency to propose a frequent subgraph mining algorithmic rule referred to as MIRAGE that uses Associate in Nursing repetitive MapReduce based mostly framework. MIRAGE is complete because it returns all the frequent subgraphs for a given user-defined support,

Literature Review - The Literature Review will focus on database management, data mining, and correlation of appropriate data sets within a networked environment.

COUNT-DECISION-TREE is an algorithm for sampling a hidden database with TOP-k-COUNT interface. In order to develop an algorithm, an attribute-order tree to a decision tree on the hidden database is generalized, since the decision tree allows each level of it to contain different attribute. With the help of figures 1 a) and b) in (Arjun Dasgupta et.al, 2009, 4),