Privacy Preservation Of The Field Of 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.

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.

“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

Data mining is used in numerous applications, particularly business related endeavors such as market segmentation, customer churn, fraud detection, direct marketing, interactive marketing, market basket analysis and trend analysis. However, since the 1993 World Trade Center bombing and the terrorist attacks of September 11, data mining has increasingly been used in homeland security efforts.

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

In addition, there are more personal data being collected as the cost of information technology falls. Although, collecting such data undeniably provides economic benefits, it has proved impossible to keep data completely protected against criminal misuse (Roberds and

“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.

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

As we live our everyday lives day to day, data is being gathered from each and every one of us; often without our consent or realization. Data is being gathered constantly when we subscribe to magazines, when we use coupons, when we use our credit cards and when we browse the Internet. Following the 9/11 attacks, the government and law enforcement proposed to develop an airline traveler screening program that would consolidate these pieces of consumer data information. It was not implemented due to its controversial nature. However, privacy and civil liberty advocates are constantly questioned, “What are you afraid of? What do you have to hide? If you haven 't done anything wrong, what 's there to worry about?". They insinuate that data is harmless, but according to law professor, Jeffrey Rosen’s book The Unwanted Gaze, you are not your profile. These datasets can contain errors. Furthermore, its misuse and revelation of information to strangers can lead to misjudgment, wrong conclusions and violations of privacy rights. In this paper, I am working on the topic of privacy in data mining operations as it relates to Kaplan’s call to balance public interest and privacy rights. The 2011 U.S. Supreme Court case, Sorrell v. IMS Health Inc. was decided on the grounds of free speech, contrary to the presumption of health data confidentiality (Kaplan, 2014). Thus, this case aids us in the understanding that there is a need to protect sensitive information, along with

In this paper, we are going to discuss about Big Data, various privacy issue in Big Data and what future holds for Big Data. We will also be focusing on how big data privacy is used in healthcare domain.

• Under the chapter XI: Offences • Section 72: Penalty for breach of confidentiality and privacy • Section 72A: Punishment for disclosure of information in breach of lawful contract:

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.