May 28, 2008 · Abstract: We present a new class of statistical de- anonymization attacks against high-dimensional micro-data, such as individual preferences, recommendations, transaction records and …

We present a new class of statistical de- anonymization attacks against high-dimensional micro-data, such as individual preferences, recommendations, transaction records and so on.

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Abstract: Datasets of large-scale industrial processes are often high-dimensional and are characterized by outliers. Probabilistic latent variable models are effective for modeling such data complexities.

In this article, we propose a solution that extends Mondrian (an efficient and effective approach designed for achieving k -anonymity) for enforcing both k -anonymity and ℓ -diversity over large datasets in a …

Jan 10, 2024 · We aim to provide valuable insights and guidelines for selecting the optimal level of anonymization that strikes a balance between recognition accuracy and privacy protection.

The two sets of large ground-truth data provide a rare opportunity to extensively evaluate a variety of de-anonymization algorithms (nine in total). We find that their performance in the real-world dataset is …

The two data sets are mainly the Gowalla-Brightkite data set and the Twitter-Foursquare data set. Second, this article explains the content of the two baseline algorithms.

Dec 29, 2022 · In a model inversion attack, an adversary attempts to reconstruct the training data records of a target model using only the model’s output. In launching a contemporary model …

Oct 29, 2015 · Abstract: Set-valued dataset contains different types of items/values per individual, for example, visited locations, purchased goods, watched movies, or search queries. As it is relatively …