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Data mining and knowledge discovery in databases are important research areas that investigate the automatic extraction of previously unknown patterns from large amounts of data. The field connects the three worlds of databases, artificial intelligence and statistics. However, the usefulness of this data is negligible if meaningful information or knowledge cannot be extracted from it. Data mining and knowledge discovery attempts to answer this need. Aiming at developing practical solutions to privacy-preserving data mining problems, we have applied the random perturbation technique and the randomized response technique. The idea is to add random noise to the original data so that it is hidden. In another field, the success of homeland security aiming to counter terrorism depends on a combination of strength across different mission areas, effective international collaboration and information sharing to support a coalition in which different organizations and nations must share some, but not all, information. Information privacy thus becomes extremely important and our technique can be applied. In the Internet era, collaborative data mining is becoming a popular way to extract useful knowledge from large databases.