On privacy preservation against adversarial data mining

Privacy preserving data processing has become an important topic recently because of advances in hardware technology which have lead to widespread proliferation of demographic and sensitive data. A rudimentary way to preserve privacy is to simply hide the information in some of the sensitive fields picked by a user. However, such a method is far from satisfactory in its ability to prevent adversarial data mining. Real data records are not randomly distributed. As a result, some fields in the records may be correlated with one another. If the correlation is sufficiently high, it may be possible for an adversary to predict some of the sensitive fields using other fields. In this paper, we study the problem of privacy preservation against adversarial data mining, which is to hide a minimal set of entries so that the privacy of the sensitive fields are satisfactorily preserved. In other words, even by data mining, an adversary still cannot accurately recover the hidden data entries. We mo...

• Type: Conference
• Year: 2006
• Where: KDD
• Authors: Charu C. Aggarwal, Jian Pei, Bo Zhang 0002
• Keywords: Adversarial Data Mining, Data Mining, KDD 2006, Privacy Preserving Data, Sensitive Fields