Preserving Private Data

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

Personal data are regulated by United Nations and urges States to implement effective measures to ensure that information concerning a person´s private life does not reach the hands of person who are not authorized by law to receive,process and use it.Thus private data are protected not only by law of States also by international laws, and concerning computer misuse

“The practice of keeping data protected from corruption and unauthorized access” is known as data security (SpamLaw, 2011). The focal point of data security is the protection of

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.

With data and the collection of it, comes the added need for security. To begin to understand how we need to secure the data we collect we need to understand a few aspects of the

Being presented with a client who has been struggling with crystal meth use for a long period of time is difficult as it is. Now, imagine working as a substance abuse counselor at an LGBQT center. As you work in this center, you are presented with this client who apart from being a crystal meth user has been attending NA and really likes the community that he has established through the group meetings there. Yet, this client feels the urge to not be fully sober and when this urge possesses him, he is prone towards calling up his old boyfriend who always has meth on him. This urge to call his ex and use meth leads him to come to your office on Monday morning with a face full of tears. In this meeting, your client informs you of his breakdown

Information privacy is referred to as data protection. It entails the existing relationship of gathering and spreading of the information, public expectation of privacy, technology and the legal circumstances pertaining to data privacy. Personal information consists of political records, financial data, website data, medical records, business related information and criminal records among others.

“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

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.

Despite the lack of a consistent understanding of the meaning of privacy, the Privacy Rule remains a time-honored and a universally accepted norm in the handling of personal health information (Nass et al, 2009). Privacy involves the collection, storage and use of a patient's personal information and determines who has access to the information and the conditions to that access. It guarantees confidentiality and security. The assurance of confidentiality prevents the physician or another health care professional who receives the information from revealing it in the course of their intimate relationship. The unauthorized or negligent disclosure can constitute a breach of that guarantee of confidentiality. And the assurance of security means that the patient's records will be kept safe from unauthorized use. Hacking into the computer system violates security and confidentiality (Nass et al).

In a health and social care setting protecting sensitive information is paramount to good care practice. It is the duty of employers to ensure that their policies and procedures adequately cover Data protection and meet the Care Quality Commission. The laws that should be followed are the Data Protection Act 1998, and the Freedom of information act 2000. The Independent Commissioners Office (I.C.O) deals primarily with breaches of information should they occur. Below is a description of the Data protection act and the Freedom of Information act. It is also the duty of employers to ensure that employer’s policies and procedures adequately cover Data protection.

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

There are four type of privacy levels. they are record-level, source-level, output-level and cryptographic privacy. First, Record-level privacy is most important in statistical surveys and setups where databases are published. Second, source-level privacy is very like record-level privacy, but the difference is privacy does not easily report the issue of privacy leaks as a result of a large number of frequent requests. Third, Output-level privacy looks directly at the result the data analysis more specifically in how much the outputs of a data removal process leak material about its inputs. This is because breaking output-level privacy means that we learned something about the private inputs that we should not have learned. Forth, Cryptographic privacy guarantees that only the last

Privacy and accuracy are contradictory to each other because improving one results in degradation of other.

In this era where internet is everything, for any purpose the corporations have to maintain large amounts of electronic data, therefore privacy of data has become our major concern .the main aim of privacy preservation is utilization of enormous amount of data present without harming the individual’s privacy. There are many effective algorithms for privacy preserving data mining but in all the approaches, some form of transformations are applied to the data in order preserve its privacy. Many methods reduce the granularity in representation in order to reduce the privacy. The transformed data set is made available