Usage

Information Collection

Once we provide information to an organization, with current laws, we have no control over its usage. Sometimes the information we provide to an organization is not needed for the purposes of our transaction with them. Other times, legitimate information collected about us is sold or used in a manner not consistent with the original purpose of that database (see Authorized Access). Participating in our society naturally generates a paper trail that we've all come to more or less accept. However, sometimes the information we are forced (or we believe we are obligated) to provide is irrelevant to the business at hand. Such was the case with a health care reform bill passed in Maryland. This bill required information about every interaction with a doctor be filed with the state, including irrelevant information such as race. The information enclosed in such files could be very damaging, including mental illness, or HIV/AIDS test results, etc. (This information would also be sold and otherwise publicly available.) (P05.06) One gets the impression that many organizations use this "extra" information to make their databases more valuable to people who might want to buy them or to improve database combination efficiency. Other organizations often "require" extraneous information. Many retailers request contact information when you make a purchase, so that they can send you junk mail. This information is not really required, although refusing can be difficult. (Full Disclosure, Sears)

Database Combination

Another possible area of database misuse is database combination. Databases are combined and cross-checked to help prevent fraud. Database combination is happening on a more frequent basis, while regulation remains weak. Combination is objectionable because it uses databases of information collected for other purposes for another purpose. There is a greater potential for abuse, as there is now suddenly twice as much data, with twice as many people with access to that data. Such combinations also heighten accuracy problems, as errors in either or both databases could lead to a painful audit. One example is the combination of a workers database with a student loans database to find people who have defaulted on their loans. An example of such combination on a large scale is the exchange of United States and Mexican tax records to try to prevent fraud by those living near the border. (R9.42)