Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
On effective classification of strings with wavelets
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
On k-anonymity and the curse of dimensionality
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Mondrian Multidimensional K-Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Injecting utility into anonymized datasets
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Scalable Clustering Algorithms with Balancing Constraints
Data Mining and Knowledge Discovery
Handicapping attacker's confidence: an alternative to k-anonymization
Knowledge and Information Systems
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
Privacy-enhanced string matching with wordwise positional sampling
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
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In recent years, privacy preserving data mining has become an important problem because of the large amount of personal data which is tracked by many business applications. An important method for privacy preserving data mining is the method of condensation. This method is often used in the case of multi-dimensional data in which pseudo-data is generated to mask the true values of the records. However, these methods are not easily applicable to the case of string data, since they require the use of multi-dimensional statistics in order to generate the pseudo-data. String data are especially important in the privacy preserving data-mining domain because most DNA and biological data are coded as strings. In this article, we will discuss a new method for privacy preserving mining of string data with the use of simple template-based models. The template-based model turns out to be effective in practice, and preserves important statistical characteristics of the strings such as intra-record distances. We will explore the behavior in the context of a classification application, and show that the accuracy of the application is not affected significantly by the anonymization process.