DataMining the Privacy and Legal Issues
DataMining Privacy and Legal Issues behind it
Thetremendous developments in information technology have resulted inthe collection as well as processing of large volumes of personaldata. This is usually exercised in criminal records, medical history,shopping habits among other habits. This aspect of data collectionhas been beneficial and useful in numerous areas such as medicalresearch, national security as well as law enforcement among otheruses. However, despite the tremendous usefulness of the informationand data obtained from the aspect of data mining, there is a growingconcern among the public about the aspect of individual privacy. Thistechnology advancement has led to the revitalized concern aboutnumerous privacy and legal issues.
Privacyand legal issues of data mining are the main reason why I haveselected this topic. The ways in which data mining can be used israising questions regarding privacy. Every year the government andcorporate entities gather enormous amounts of information aboutcustomers, storing it in data warehouses. Part of the concern is thatonce data is collected and stored in a data warehouse, who will haveaccess to this information? Often times a consumer may not be awarethat the information collected about him/she is not just shared withwho collected the information. With technologies that are availabletoday, data mining can be used to extract data from the datawarehouses, finding different information and relationships aboutcustomers and making connections based on this extraction, whichmight put customer information and privacy at risk. Data miningnecessitates data arrangements that can cover consumer information,which may compromise confidentiality and privacy. One way for this tohappen is through data aggregation where data is accumulated fromdifferent sources and placed together so that they can be analyzed. Iwill address three critically important questions/issues affectingdata mining today, these are (I)Ethical Concerns (ii) Security Concerns (iii) Maintaining DataIntegrity
Theuse of data mining, especially data about people, has serious ethicalimplications. Companies face an ethical dilemma when even deciding ifthe company should make a person aware his/her information is beingstored for future data mining. By giving a person the option to optout of data collection, a company can hurt its competitive advantagein a market place. A company must decide if a lack of ethical concernwill cause a loss of good will from consumers and suffer from abacklash from the company’s consumers (Rajaretnam, 2014). Companieswho use data mining techniques must act responsibly by being aware ofthe ethical issues that are surrounding their particular applicationthey must also consider the wisdom in what they are doing. Forexample, data mining sometimes can be used to discriminate people,especially regarding racial, sexual and religious orientations.However, despite the need to look at the ethical aspect of datamining, most of the companies in the USA have not yet been met withthe tougher regulations that are already in place in areas such asEurope (Rajaretnam, 2014).
Thereare laws, both local and federal those are meant to control thecollection, sale and use of data about the customers in the country.This is owing to the fact there is a lot of pressure that comes fromthe fact that most of the other countries across the globe have beenable to make major strides in their quest to protect customerinformation. The rate at which, the world business is changing everyday calls for something to be done on the same. This is because ofthe interaction that consumers in the US are continuously having withcompanies that are based in other countries such as the EuropeanUnion. This has led to an increased personal and class-actionlitigation that are levied against businesses as a result of datamining (Maimon,& Rokach, 2005).
Datamining is the process of creating a sequence of correct andmeaningful queries to extract information from large amounts of datain the database. As we know, data mining techniques can be useful inrecovering problems in database security. However, with the growth ofdevelopment, it has been a serious concern that data miningtechniques can cause security problems (Maimon,& Rokach, 2005).A lot of security experts see data mining as one of the most primarychallenges that consumers will encounter in the next decade. Thedefinite complexity in data mining is building up accurate models fordata analysis without giving the right to use the information inspecific customer records, which will secure the database from beingused the wrong way. Data mining is one of the ways that people can beable to get facts that are not obvious to human analysts of the data.Research has indicated that data mining enables inspection andanalysis of huge amounts of data (Rajaretnam, 2014). Advancements intechnology have led to possible security issues that are attributedto data mining. It is for this reason that most of the people arealways concerned with the way companies’ use consumer information.One of the major threats is the possibility of predicting informationabout classified work from being correlated with unclassified work. Agood example of this is the use of budgets and staffing. There isalso the possibility of detection of hidden information based on theconspicuous lack of information (Maimon,& Rokach, 2005). The use of mining open source data that can be used to determinepredictive events, which can be used in ways that threaten nationalsecurity. In most cases, terrorism is one of the issues that arebrought about by data mining. Terrorists can use the data on thedeliveries that are made in various sections of the federalgovernment. This information can be used to organize major attacks inthe country.
Itis important to note that it is not the data we are protecting butthe correlations among the data items (Dimitrakakis,2011).There are so many ways that data that is mined can be used. Thegovernment can be able to use the data for many monitoring actionsthat the consumers may not be okay with. This is the reason as to whymost of the citizens never agree with the idea of their informationbeing stored in warehouses where it can be accessed in the future forvarious reasons. Most of them fear that this data may be mined forthings that are not in line with their wishes. Consumers should begiven a chance to know the kind of information that is kept aboutthem, which is not the case when it comes to current version of datamining that we are having today (Terano,Liu & Chen, 2000).
Ensuringdata integrity is a key factor to ensure that data mining tools andanalysis is meaningful and accurate. Data integrity ensures that datais consistent throughout the database. There are several businessrules (also known as constraints) that maintain the accuracy andintegrity of data stored in the database. For data to be consideredreliable and accurate, it has to be complete with no violations orcompromises from the original data. Compromises of data can occur invarious, which makes identifying and addressing potential causes ofdamage as one of the major tasks that are performed in any companythat holds a lot of data on its consumers (Dimitrakakis,2011).
Oneof the challenges in implementation is the integration of conflictingor redundant data that comes from different sources. A good exampleis when a bank can store information in different databases. Therehas to be software that can translate data from one system toanother. One of the debates in data mining is whether it is a prudentidea to use a relational database or multidimensional database. In arelational database, data is stored in tables and allows one make adhoc queries. In a multidimensional structure, data is stored in setsof cubes are arranged in arrays where subsets are created accordingto category. Relational structures are known to perform better in aclient/ server environment (Terano,Liu & Chen, 2000).
However,integrity may not be affected as a result of some challenges that mayaffect the way things are done. In most cases, there are manyviolations of integrity that may appear as a result of severalreasons. One of the reasons is the fact that there might be hardwareand software errors. Manufacturing of the hardware or software maylead to data corruption. There may also be a serious damage to storeddata that may be brought about by the malfunctioning of the hardwarethat may lead to a malfunctioning of the software. Maliciousintrusions are increasing as a result of the fact that new securityvulnerabilities are arising on daily bases. This means that data canbe accessed from remote locations through un-trusted links(Dimitrakakis,2011).
Theevolution and advancement in the information technology sector havebrought along divided views among its users. Despite the tremendousbenefits associated with IT development, there are related concernsin reference to individual privacy. This has in turn led to ethicalconcerns, and security concerns behind the practice of data mining.It is evident that, there is needs for the decision makers have anunderstanding of the key strategic issues behind data mining. Lawsgoverning people’s privacy should be reviewed, in order to preventunwarranted exposure of individual privacy, through illegal datamining.
Dimitrakakis,C. (2011). Privacyand Security Issues in Data Mining and Machine
Learning.S.l:Springer Berlin / Heidelberg.
Maimon,O. & Rokach, L. (2005). Datamining and knowledge discovery handbook.New
Rajaretnam,T. (2014). Data Mining and Data Matching: Regulatory and Ethical
ConsiderationsRelating To Privacy and Confidentiality in Medical Data. Journal ofInternational Commercial Law & Technology, 9(4),294-310.http://www.dhs.gov/sites/default/files/publications/privacy/Reports/2012-data-mining-report-to-congress.pdf
Terano,T., Liu, H. & Chen, A. (2000). Knowledgediscovery and data mining: current issues
andnew applications: 4th Pacific-Asia Conference, PAKDD 2000, Kyoto,Japan, April 18-20, 2000 : proceedings.Berlin New York: Springer.
Williams, G. J.,Altas, I., Bakin, S., Christen, P., Hegland, M.,Marquez, A., Roberts, S.
(1999).The Integrated Delivery of Large-Scale Data Mining: The ACSys DataMining Project.