Data Mining - Mehmed Kantardzic [218]
At the same time, especially since 9/11, government agencies have been eager to experiment with the data-mining process as a way of nabbing criminals and terrorists. Although details of their operation often remain unknown, a number of such programs have come to light since 2001. The Department of Justice (DOJ), through the Federal Bureau of Investigation (FBI), has been collecting telephone logs, banking records, and other personal information regarding thousands of Americans not only in connection with counterterrorism efforts, but also in furtherance of ordinary law enforcement. A 2004 report by the Government Accountability Office (GAO) found 42 federal departments—including every cabinet-level agency that responded to the GAO’s survey—engaged in, or were planning to engage in, 122 data-mining efforts involving personal information (U.S. General Accounting Office, Data Mining: Federal Efforts Cover a Wide Range of Uses [GAO-04-548], May 2004, pp. 27–64). Recently, the U.S. Government recognized that sensible regulation of data mining depends on understanding its many variants and its potential harms, and many of these data-mining programs are reevaluated. In the United Kingdom, the problem is being addressed more comprehensively by the Foundation for Information Policy Research, an independent organization examining the interaction between information technology and society with goals to identify technical developments with significant social impact, commission research into public policy alternatives, and promote public understanding and dialog between technologists and policy makers in the United Kingdom and Europe. It combines information technology researchers with people interested in social impacts, and uses a strong media presence to disseminate its arguments and educate the public.
There is one additional legal challenge related specifically to data mining. Today’s privacy laws and guidelines, where they exist, protect data that are explicit, confidential, and exchanged between databases. However, there is no legal or normative protection for data that are implicit, nonconfidential, and not exchanged. Data mining can reveal sensitive information that is derived from nonsensitive data and meta-data through the inference process. Information gathered in data mining is usually implicit patterns, models, or outliers in the data, and questionable is the application of privacy regulations primarily written for traditional, explicit data.
In addition to data privacy issues, data mining raises other social concerns. For example, some researchers argue that data mining and the use of consumer profiles in some companies can actually exclude groups of customers from full participation in the marketplace and limit their access to information.
Good privacy protection not only can help build support for data mining and other tools to enhance security, it can also contribute to making those tools more effective. As technology designers, we should provide an information infrastructure that helps society to be more certain that data-mining power is used only in legally approved ways, and that the data that may give rise to consequences for individuals are based on inferences that are derived from accurate, approved, and legally available data. Future data-mining solutions reconciling any social issues must not only be applicable to the ever changing technological environment, but also flexible with regard to specific contexts and disputes.
12.7 REVIEW QUESTIONS AND PROBLEMS
1. What are the benefits in modeling social networks with a graph structure? What kind of graphs would you use in this case?
2. For the given undirected graph G:
(a) compute the degree and variability parameters of the graph;
(b) find adjacency matrix for the graph G;
(c) determine binary code(G) for the graph;
(d) find closeness parameter or each node of the graph; and
(e) what