Privacy Preserving Data Mining : Database Security And Privacy

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

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.

Health care facilities, physicians, health care personnel and most importantly patients will definitely benefit from the data mining health care information. This paper will discuss different ways data mining health care information will be beneficial to health care facilities, health care personnel and patients alike and also the risk of data mining health care 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

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.

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

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.

“If we destroy human rights and rule of law in response to terrorism, they have won”(Joichi Ito). Ever since technology has been available widely around the world, privacy has been a problem. recently , government officials had admitted to collecting data from online databases. The government should not be continuing this violation of privacy for the following reasons, privacy is a basic human right, it is not insured that the people viewing the data will not be corrupt, and because people will no longer be creative or have ideas of their own.

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.

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.

Outsourcing data mining computations to a third-party service provider (server) offers a cost-effective solution mostly for data owners (clients) of limited resources. Such a structure introduces the data-mining-as-a-service (DMaS) paradigm. Now Cloud computing provides a natural solution for