Practical data-swapping: the first steps
ACM Transactions on Database Systems (TODS)
A data distortion by probability distribution
ACM Transactions on Database Systems (TODS)
TRUSTe: an online privacy seal program
Communications of the ACM
Privacy interfaces for information management
Communications of the ACM
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
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
Some geometric clustering problems
Nordic Journal of Computing
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
On the Privacy Preserving Properties of Random Data Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A Framework for High-Accuracy Privacy-Preserving Mining
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Top-Down Specialization for Information and Privacy Preservation
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
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
Practical privacy: the SuLQ framework
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Privacy-enhancing k-anonymization of customer data
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
To do or not to do: the dilemma of disclosing anonymized data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
On k-anonymity and the curse of dimensionality
VLDB '05 Proceedings of the 31st international conference on Very large data bases
IEEE Transactions on Knowledge and Data Engineering
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Privacy via pseudorandom sketches
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Personalized privacy preservation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Anonymizing Classification Data for Privacy Preservation
IEEE Transactions on Knowledge and Data Engineering
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Anonymity preserving pattern discovery
The VLDB Journal — The International Journal on Very Large Data Bases
Providing k-anonymity in data mining
The VLDB Journal — The International Journal on Very Large Data Bases
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
Testing software in age of data privacy: a balancing act
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
Permutation anonymization: improving anatomy for privacy preservation in data publication
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Privacy-preserving back-propagation and extreme learning machine algorithms
Data & Knowledge Engineering
A Privacy Preserving Method Using Privacy Enhancing Techniques for Location Based Services
Mobile Networks and Applications
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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. In many cases, users are unwilling to provide personal information unless the privacy of sensitive information is guaranteed. In this paper, we propose a new framework for privacy-preserving data mining of multidimensional data. Previous work for privacy-preserving data mining uses a perturbation approach which reconstructs data distributions in order to perform the mining. Such an approach treats each dimension independently and therefore ignores the correlations between the different dimensions. In addition, it requires the development of a new distribution-based algorithm for each data mining problem, since it does not use the multidimensional records, but uses aggregate distributions of the data as input. This leads to a fundamental re-design of data mining algorithms. In this paper, we will develop a new and flexible approach for privacy-preserving data mining that does not require new problem-specific algorithms, since it maps the original data set into a new anonymized data set. These anonymized data closely match the characteristics of the original data including the correlations among the different dimensions. We will show how to extend the method to the case of data streams. We present empirical results illustrating the effectiveness of the method. We also show the efficiency of the method for data streams.