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<p>Microaggregation is a powerful technique for safeguarding data, enabling us to strike a balance between the risk of disclosing sensitive information and the loss of valuable information. It is a crucial tool for data sharing that provides <i>k</i>-anonymity. With the growing prevalence of multi-view data, there is an increasing interest in protecting such data using appropriate techniques. To the best of our knowledge, this paper introduces the first approach specifically designed for multi-view data protection. We present a novel approach to microaggregation by introducing multi-view fuzzy c-means, which allows us to consider linear constraints on the variables in each view that describe the data. Our method ensures that the resulting clusters adhere to these constraints, even when the data being masked fails to satisfy them. This approach not only enhances data privacy by maintaining <i>k</i>-anonymity in multi-view contexts but also preserves the structural integrity of the data across different views. Our contributions include the development of a multi-view clustering framework with built-in privacy safeguards and the introduction of linear constraints to ensure consistency across multiple views. This innovative approach provides a robust solution for the privacy-preserving analysis and sharing of multi-view data.</p>

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Fuzzy clustering-based microaggregation for multi-view data with constraints

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月12日

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  • Fatemeh Sadjadi,
  • Vicenç Torra

摘要

Microaggregation is a powerful technique for safeguarding data, enabling us to strike a balance between the risk of disclosing sensitive information and the loss of valuable information. It is a crucial tool for data sharing that provides k-anonymity. With the growing prevalence of multi-view data, there is an increasing interest in protecting such data using appropriate techniques. To the best of our knowledge, this paper introduces the first approach specifically designed for multi-view data protection. We present a novel approach to microaggregation by introducing multi-view fuzzy c-means, which allows us to consider linear constraints on the variables in each view that describe the data. Our method ensures that the resulting clusters adhere to these constraints, even when the data being masked fails to satisfy them. This approach not only enhances data privacy by maintaining k-anonymity in multi-view contexts but also preserves the structural integrity of the data across different views. Our contributions include the development of a multi-view clustering framework with built-in privacy safeguards and the introduction of linear constraints to ensure consistency across multiple views. This innovative approach provides a robust solution for the privacy-preserving analysis and sharing of multi-view data.