Privacy Preserving Data in Data Mining

According to the text, data mining is becoming increasing common in both the private and public sectors. Industries such as banking, insurance, medicine, and retailing, commonly use data mining to reduce costs, enhance research and increase sales. In the public sector, data-mining applications initially were used as a mean to detect fraud and waste, but have grown to also be used for purposes such as measuring and improving program performance.

Nowadays, data mining is an emerging field, connecting the three worlds of databases, artificial intelligence and statistics. The current information revelation gives many organizations the opportunity to gather hug amounts of data as needed. Data mining consider as a knowledge discovery used to answer any data needs.

Data mining - non-trivial extraction of implicit, previously unknown, and potentially useful information from large data sets or databases.

Privacy preserving data mining - study of achieving some data mining goals without scarifying the privacy of the individuals.

A data owner wants to release a person-specific data table to another party or to the public for the purpose of classification analysis without scarifying the privacy of the individuals in the released data.

Data mining techniques

Data mining techniques are used in business and research and are becoming more and more popular with time. Data Mining produces a number of techniques to perform the data mining tasks in a privacy-preserving way and to set where very large databases are involved. These techniques are

1. Data modification techniques.
2. Cryptographic methods and protocols for data sharing.
3. Statistical techniques for disclosure and inference control search for interesting information without demanding a priori hypotheses.
4. Query auditing methods.
5. Randomization.
6. Perturbation-based techniques. 

Privacy Preserving Data Mining is designed for researchers, professors, and advanced-level students in computer science.

References
Acquisti, A., Gritzalis, S., Lambrinoudakis, C., & Vimercati, S. D. C. d. (Eds.). (2008). Digital Privacy: Theory, Technologies, and Practices. New York: Auerbach Publications.