The Privacy Preserving Data Mining

Privacy in this era is threatened by the growth in technology with capacity that is enhanced for surveillance, storage, communication as well as computation. Moreover, the increased value of this information in decision making is one of the insidious threats. For this reason, information and its privacy are actually threatened and less privacy is assured.

Some health institutions believe that all the patients have the powers to control the use of their records and before any file is accessed, the patient must be consulted by the personnel responsible. To others, however, some of the patients may not know the needs of the health industry, and therefore, at least 200 people can be allowed to access their records. According to this group, the only way to improve the patient’s privacy is by reducing the number of people who access the records. Thus, despite the fact that digital files save on cost and time, there is need to focus on some of the issues affecting the privacy of records in the health sector. Therefore, as much as the current law allows sharing of patient information during payments and treatment, caution must be taken to reduce data mining and marketing using the same

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

Privacy, a term that is reluctant in today’s modernized society who’s meaning is far from realistic. As the world congruently fosters a new way of living, through technology our lives as we know as our own is far from it. This paper will discuss the use of technology to research ones private information, the advantages and disadvantages of the public access of such information and the laws that promulgate such data.

Data mining is defined as the process of data selection and studying and building models using massive data stores to disclose previously unidentified patterns in databases (Koh and Tan, 2005, p. 64). Koh and Tan have found financial institutions, marketers, manufactures and so has many other agencies have used data mining. Data mining has been of great use by various organizations. For example, data mining has been useful to detect fraudulent credit card transactions (Koh and Tan, 2005, p. 64). Koh and Tan stated, “In healthcare, data mining is becoming increasingly popular, if not increasingly essential” (Koh and Tan, 2005, p.64). In healthcare there have been reports that data mining has been successful in detecting fraud and abuse in healthcare claim (Koh and Tan, 2005, p.65). There are many factors in healthcare that have driven the use of data mining applications. One of the factors that have driven healthcare to use the data mining applications will be the medical insurance fraud and abuse. All organizations currently involved in the healthcare industry can profit from the data mining applications. For example, data mining is able to help

“If we wanted to figure out if a customer is pregnant, even if she didn’t want us to know, can you do that?,” asked by Andrew Pole’s colleagues. In today’s day and age of technology, data mining can be easily used to compile huge capacities of data that is validated to calculate patterns of the data from the information such as name, address, date of birth, credit card numbers, and social security numbers that people have submitted to the Internet through purchases, advertising, and profiles everyday. Although data mining seems harmless, it allows companies to gather information to improve the business by making ethical decisions; therefore, this can raise concerns with privacy and security of the person and/or their personal information that

Privacy, specifically the sharing of one’s personal information with and without permission, is at the forefront of many conversations today. This topic is a critical aspect of societal existence in the technologically driven 21st century. There’s a plethora of data stored and used in nearly every business operation; and the stored data is often sold to and/or shared with other parties/entities. The management and protection of an individual’s personally identifiable information is paramount to individual and national security.

Information privacy is related to personal data that is usually stored on computers and online servers (Litman, 2000). However, the information that internet users share online is not private as companies use this information or sell it to the other companies and governments who are interested in the information (Litman, 2000). Before the introduction of the internet and possibly the World Wide Web, private information was limited to the owner. Today, technologies used by online companies in tracking up personal information are so advanced that individuals cannot have control of them.

In order for a business to succeed in selling a product or service, they must first know their target market. There are many ways to find information about customer preferences, the most effective being a process called ‘data mining.’ Data mining is “obtaining detailed, real-time customer information via internal databases” (Morris & Pharr, Invasion of privacy: A dilemma for marketing research and database technology, 1992, p. 11). A relatively new practice, data mining for marketing has increasingly brought the business and information technology fields together. However, it has also become a controversial practice due to concerns that companies are crossing the line between effective advertisement and respect for the privacy of individuals. In this paper, I argue that data mining of personal data for marketing purposes has become unacceptably invasive of people’s private information, as with the new practice of collecting information from private emails in order to market advertisements. Most companies will continue to push the envelope, using stored information in customer databases in an unethical fashion, unless companies that successfully find a balance between marketing advantages and privacy rights of individuals lead by example.

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

Personal privacy today is a controversial and complex topic, which is influenced by a number of factors. There is an integral role that databases play in this highly debated topic. The fact that many people now carry out their transactions electronically is another important factor. There is also pressure on personal privacy for increased national security around the world to combat terrorism. In addition, personal privacy is even threatened by commercial factors and the Internet.

Today with the ever growing use of computers in the world, information is constantly moving from one place to another. What is this information, who is it about, and who is using it will be discussed in the following paper. The collecting, interpreting, and determination of use of this information has come to be known as data mining. This term known as data mining has been around only for a short time but the actual collection of data has been happening for centuries. The following paragraph will give a brief description of this history of data collection.

Many practical situations arise when privacy of data becomes a concern. On the other hand knowing the result of common computation is in their mutual interest. Consider following scenario: Four brothers living independently want to know the total wealth of family but no brother wants to disclose his individual wealth. All the students in a class want to know the average marks obtained by students but no student is willing to show his marks to others. Certain number of mobile phone companies wants to know the total customers in an area but no company want to disclose its number of customers. SMC concept was introduced by Yao [1] where he gave a solution to two millionaire’s problem. Each of the millionaires wants to know who is richer without disclosing individual wealth. After that the subject has taken many branches like privacy preserving statistical analysis,

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.

My specific purpose is to inform my audience to be conscious of their privacy of personal data in daily life because there are many powerful commercial enterprises which are taking advantage in human by using our personal data for diverse reasons beyond our knowledge. In order to resolve this problem, I will give the way to live our life by protecting our personal data from the violators. Besides, I would like to specifically inform to the powerful organizations that treat the others ' personal data dealing with them based on basic human right and conscience.