Privacy Protecting Confidential Information On The Security Of The Confidential Data

In this document I will be discussing the laws that are related to security and privacy of datas, I will explain how they relate to the security and privacy of data.

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

Recommender engines gather information about users such as their demographic information, location, and many other usable data to provide an accurate recommendation. It is essential for recommendation system to use some algorithms and programs to protect the privacy of user information as well as make this process more secure, reliable and confidential. (Asanov,

Database Analysis Computers are a key tool in the fight against terrorism. Many experts believe that technology will be able to sort through mountains of data collected from intercepted phone calls, travel records, and watch lists. According to FCW.com, data mining and analytical software can be applied to databases to combine through vast amounts of digital information, discover patterns, and identify relationships that could indicate criminal activity. Similar technologies are commonly used by

of big data are obvious but we also have to ensure our privacy at the same time. If we ensure privacy in

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

and analyzing enormous sets of data and then extracting the meaning of the data. Data mining

Information classification assure confidentiality using a predefined measure to classify and handle information and whether it is allowed for distribution or not.

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

Privacy is a major problem these days. Cheaper memory has led to immense amount of data continuously being accumulated in various databases across the world, from universities, companies to patient records, content and link graphs of social net-works, mobility traces in cellular networks, book and movie ratings, etc.—and there are many socially valuable uses to which it can potentially be put. But, as Netflix and others have discovered, even when data collectors try to protect the privacy of their customers by releasing anonymized or aggregated data, this data often reveals much more than intended, especially when it is combined with other data sources. This calls for the need to immediately

Data mining techniques provide good results only if input data is accurate. But data collected from users are often inaccurate.Users may deliberately enter inaccurate information if they are asked to provide personal information because of their worry that information may be misused by organisation to harass them. So we need to find data mining techniques that encourages the user to provide accurate information by ensuring them that data mining techniques used will not voilate their privacy and also produce accurate mining results.

Privacy-preserving data publishing provides methods and tools for publishing useful information and also for preserving data privacy. Privacy-preserving data publishing was intended to construct published tables in a way that allows systematic tasks (e.g., aggregate query answering, data mining) over the published data and also for protecting the privacy of individual’s data.

Currently, one of the major concerns is privacy to prevent the private data which they don’t want to share. This paper targeted on the previous literatures in the domain of Privacy Preserving Data Mining. From this analysis, it has been found that there is no only one method which is constant in all fields. All techniques operate in a separate way based on the pattern

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.