Self-organizing maps
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Top-Down Specialization for Information and Privacy Preservation
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Two methods for privacy preserving data mining with malicious participants
Information Sciences: an International Journal
Knowledge discovery on RFM model using Bernoulli sequence
Expert Systems with Applications: An International Journal
An analysis of privacy signals on the World Wide Web: Past, present and future
Information Sciences: an International Journal
IEEE Transactions on Knowledge and Data Engineering
A novel anonymization algorithm: Privacy protection and knowledge preservation
Expert Systems with Applications: An International Journal
Preservation of Data Privacy Using PCA Based Transformation
ARTCOM '09 Proceedings of the 2009 International Conference on Advances in Recent Technologies in Communication and Computing
Privacy-Preserving Multiparty Collaborative Mining with Geometric Data Perturbation
IEEE Transactions on Parallel and Distributed Systems
HHUIF and MSICF: Novel algorithms for privacy preserving utility mining
Expert Systems with Applications: An International Journal
Privacy-preserving data mining: A feature set partitioning approach
Information Sciences: an International Journal
A time-efficient pattern reduction algorithm for k-means clustering
Information Sciences: an International Journal
Data clustering by minimizing disconnectivity
Information Sciences: an International Journal
Extending l-diversity to generalize sensitive data
Data & Knowledge Engineering
Enhanced clustering of biomedical documents using ensemble non-negative matrix factorization
Information Sciences: an International Journal
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Data processing techniques and the growth of the internet have resulted in a data explosion. The data that are now available may contain sensitive information that could, if misused, jeopardise the privacy of individuals. In today's web world, the privacy of personal and personal business information is a growing concern for individuals, corporate entities and governments. Preserving personal and sensitive information is critical to the success of today's data mining techniques. Preserving the privacy of data is even more crucial in critical sectors such as defence, health care and finance. Privacy Preserving Data Mining (PPDM) addresses such issues by balancing the preservation of privacy and the utilisation of data. Traditionally, Geometrical Data Transformation Methods (GDTMs) have been widely used for privacy preserving clustering. The drawback of these methods is that geometric transformation functions are invertible, which results in a lower level of privacy protection. In this work, a Principal Component Analysis (PCA)-based technique that preserves the privacy of sensitive information in a multi-party clustering scenario is proposed. The performance of this technique is evaluated further by applying a classical K-means clustering algorithm, as well as a machine learning-based clustering method on synthetic and real world datasets. The accuracy of clustering is computed before and after privacy-preserving transformation. The proposed PCA-based transformation method resulted in superior privacy protection and better performance when compared to the traditional GDTMs.