Data Mining_ Concepts and Techniques - Jiawei Han [374]
The focus of data mining technology is on the discovery of general or statistically significant patterns, not on specific information regarding individuals. In this sense, we believe that the real privacy concerns are with unconstrained access to individual records, especially access to privacy-sensitive information such as credit card transaction records, health-care records, personal financial records, biological traits, criminal/justice investigations, and ethnicity. For the data mining applications that do involve personal data, in many cases, simple methods such as removing sensitive IDs from data may protect the privacy of most individuals. Nevertheless, privacy concerns exist wherever personally identifiable information is collected and stored in digital form, and data mining programs are able to access such data, even during data preparation.
Improper or nonexistent disclosure control can be the root cause of privacy issues. To handle such concerns, numerous data security-enhancing techniques have been developed. In addition, there has been a great deal of recent effort on developing privacy-preserving data mining methods. In this section, we look at some of the advances in protecting privacy and data security in data mining.
“What can we do to secure the privacy of individuals while collecting and mining data?" Many data security–enhancing techniques have been developed to help protect data. Databases can employ a multilevel security model to classify and restrict data according to various security levels, with users permitted access to only their authorized level. It has been shown, however, that users executing specific queries at their authorized security level can still infer more sensitive information, and that a similar possibility can occur through data mining. Encryption is another technique in which individual data items may be encoded. This may involve blind signatures (which build on public key encryption), biometric encryption (e.g., where the image of a person's iris or fingerprint is used to encode his or her personal information), and anonymous databases (which permit the consolidation of various databases but limit access to personal information only to those who need to know; personal information is encrypted and stored at different locations). Intrusion detection is another active area of research that helps protect the privacy of personal data.
Privacy-preserving data mining is an area of data mining research in response to privacy protection in data mining. It is also known as privacy-enhanced or privacy-sensitive data mining. It deals with obtaining valid data mining results without disclosing the underlying sensitive data values. Most privacy-preserving data mining methods use some form of transformation on the data to perform privacy preservation. Typically, such methods reduce the granularity of representation to preserve privacy. For example, they may generalize the data from individual customers to customer groups. This reduction in granularity causes loss of information and possibly of the usefulness of the data mining results. This is the natural trade-off between information loss and privacy. Privacy-preserving data mining methods can be classified into the following categories.
■ Randomization methods: These methods add noise to the data to mask some attribute values of records. The noise added should be sufficiently large so that individual record values, especially sensitive ones, cannot be recovered. However, it should be added skillfully so that the final results of data mining are basically preserved. Techniques are designed to derive aggregate distributions from the perturbed data. Subsequently, data mining techniques can be developed to work with these aggregate distributions.
■ The k-anonymity and l-diversity methods: Both of these methods alter individual records so that they cannot be uniquely identified. In the k-anonymity method, the granularity of data representation is reduced sufficiently so that