In data mining, the privacy and legal issues that may result are the main keys to the growing conflicts. The ways in which data mining can be used is raising questions regarding privacy. Every year the government and corporate entities gather enormous amounts of information about customers, storing it in data warehouses. Part of the concern is that once data is collected and stored in a data warehouse, who will have access to this information? Oftentimes a consumer may not be aware that the information collected about him/her is not just shared with who collected the information. With the technologies that are available today, data mining can be used to extract data from the data warehouses, finding different information and relationships about customers and making connections based on this extraction, which might put customer’s information and privacy at risk. Data mining necessitates data arrangements that can cover consumer’s information, which may compromise confidentiality and privacy. One way for this to happen is through data aggregation where data is accumulated from different sources and placed together so that they can be analyzed.
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Companies such as IBM are working on methods of mining data that will allow for complete individual privacy while still creating accurate models of data. IBM’s method has developed a method called Privacy-Preserving Data Mining. By randomizing a consumer’s personal information before it is ever transmitted using IBM’s Privacy-Preserving Data Mining Method, a company can still gather the information it would like while not impeding on its customer’s right of privacy.
It is logical that a lot of companies and governmental agencies need to use data mining as a part of their jobs, but the hesitation is if this information is being used the right way. For example, data mining can be helpful for some companies in order to target the right market. In the technological and the informational age it looks like the process of getting data about customers and employees is getting a lot easier than it used to be before. The quick transfer of personal information has resulted to identity theft risks. Privacy concerns are becoming an important issue in data mining because of the risks behind it, especially that many of the consumers who buy products or services are not conscious of data mining technology.
6.2 Ethical Concerns
The use of data mining, especially data about people, has serious ethical implications. Companies face an ethical dilemma when even deciding if the company should make a person aware his/her information is being stored for future data mining. By giving a person the option to opt out of data collection, a company can hurt its competitive advantage in a market place. A company must decide if a lack of ethical concern will cause a loss in good will from consumers and suffer from a backlash from the company’s consumers. Companies who use data mining techniques must act responsibly by being aware of the ethical issues that are surrounding their particular application; they must also consider the wisdom in what they are doing. For example, data mining sometimes can be used to discriminate people, especially regarding racial, sexual and religious orientations. The use of data mining in this way is not only considered unethical, but also illegal. Individuals need to be protected from any unethical use of their personal information, and before they make any decision to provide their data they need to know how this information will be used, why is it being use, what parts of the information are going to be taken, and what consequences this action will have. By doing this, Individuals will be informed and told straightforwardly about the reasons and consequences of using their information. Ethical concerns in data mining can be seen in two main ethical themes and these relate to privacy and individuality. As mentioned previously, the wrong use of data can cause people to fall in unethical issues, which are also considered illegal. The importance of privacy and individuality has to be valued and believe protected to make sure that people are treated reasonably. People should be conscious of the significance of the threats and dangers and constantly discuss these ethical issues. Experts consider data mining to be morally neutral, on the other hand, the way that this data is being used may come up with questions and concerns about ethics. Data need to be used in the right purpose to make sure people are safe.
6.3 Security Concerns
Data mining is the process of creating a sequence of correct and meaningful queries to extract information from large amounts of data in the database. As we know, data mining techniques can be useful in recovering problems in database security. However, with the growth of development, it has been a serious concern that data mining techniques can cause security problems. A lot of security experts see data mining as one of the most primary challenges that consumers will encounter in the next decade. The definite complexity in data mining is building up accurate models for data analysis without giving the right to use the information in specific customer records, which will secure the database from being used the wrong way. Developing such models can reduce the security issues that users may face. Security problems in data mining are one of the most popular concerns because of the fact that when using data mining individuals are usually working with large amount of information, and they can have access to it easily. This is dangerous if this data was not used in a secure way. As data mining guarantees to open up lots of new fields for extracting information from both old databases and future databases that may be developed with data mining as a support purpose, the data mining session in some large companies suggest that there can be serious security issues in data mining. While saying this, it is not to recommend that data mining should be illuminated, however, it is to mention security as one of important aspects and issues that should be judged and addressed.
Data warehousing companies must monitor who has access to the data within and what parts of the data warehouse they have access to. An example of a company that allows restricted access to their data warehouse for data mining purposes is Wal-Mart. Wal-Mart has a very extensive database of all their stock, stores, and collected data. Companies that have products carried by Wal-Mart are allowed into Wal-Mart’s database. This allows these companies to mine this data for information regarding the sale of their products. By restricting the accessibility of these companies to just the products offered by the companies, Wal-Mart shows that it is aware of the concerns for security and privacy when it comes to data mining.
6.4 Maintaining Data Integrity
Ensuring data integrity is a key factor to ensure that data mining tools and analysis is meaningful and accurate. Data integrity ensures that data is consistent throughout the database. There are several business rules (also known as constraints) that maintain the accuracy and integrity of data stored in the database.
Domain constraints focus on what values may be assigned to an attribute. Upon the creation of a database, each attribute must contain domain name, data type (such as numeric, character, date, or integer), size, and the acceptable range or value of the data.
Entity integrity ensures that every primary key is a non-null. Also every attribute that is part of the primary key within a data base is non-null as well. If a value is not known a database developer will create a null (an automatic value that is assigned if no information is available) value.
Referential integrity states that each foreign key value must be identical to a primary key value. Suppose that a database exists with a database table titled customer. The customer will be assigned a customer ID. The customer ID will ideally then become the primary key. Now, suppose the customer places an order within the company. The Order table should contain the attribute customer-ID as well which will be identified as the foreign-key of the Order table. Using referential integrity will guarantee that when the customer ID is queried, that only the customer who exists with the specified ID is shown and what specific order that particular customer has placed.
Combining the domain, entity, and referential integrity rules will diminish the redundancy of database information and allow the users to modify and delete error and inconsistencies.
Integrity controls are placed within a database to protect the database from unauthorized updates and inputs. Assertions are created so specific rules that are standard within a business are implemented through the database (such as Sarbanes-Oxley mandates). Trigger controls are created so that if an event occurs (such as a late payment) a specific action will occur (such as a late fine added to an account).
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Authorization rules are created to so that there are restrictions on who may be able to view the data, enter the date, change the date, and delete the data. Authorization rules are used to protect the data from the chance of an employee to alter data that their job has no authorized capacity in doing so. It also protects a person’s information (such as credit card numbers and addresses) that is contained within a database to not be read by unauthorized employees.
It is essential that integrity controls and rules are placed within databases so that the data may maintain its usefulness and security protection. If integrity constraints were not implemented within a database, any information that could be generated from the database would be useless. This in turn would be useless for any data mining procedures and analysis.
7 Data Mining Laws and Compliance Regulations Enacted
7.1 Federal Agency Data Mining Reporting Act of 2007
The Federal Agency Data Mining Report Act of 2007 was enacted to require government agencies to report to their data mining activities to Congress. The act reads that any department of agency of the Federal Government or any agency associated doing work for the Federal Government “shall submit a report to Congress on all such activities of the department or agency under the jurisdiction of that official.” The report must include the activities goals, dates data mining was deployed, a description of how data mining was used and the “the basis for determining whether a particular pattern or anomaly is indicative of terrorist or criminal activity.” As well as a description of data sources, assessment of efficacy of the data mining in providing accurate information, its impact on privacy or civil liberties and laws or regulations that government the information. 
7.2 Prescription Data Mining Legislation
Several states including New Hampshire, Vermont, and Maine has enacted laws or acts preventing the use of patient data for data mining. The laws prevent the sale or use of such information in their states. In 2006, New Hampshire added to law the Prescription Confidentiality Act. This act require that patient data be kept confidential and that such data could not be “licenses, transferred, used, or sold by an pharmacy benefits manager, insurance company, electronic transmission intermediary â€¦ except for the limited purpose of pharmacy reimbursement.”  After multiple court hearings, Main had it’s Prescription Data Law upheld. The law allows “physicians and other prescribers to opt out of having their prescribing data sold to health information firms and drugmakers for marketing purposes.” The law came about after New Hampshire’s Prescription Confidentiality Act. 
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