Titlebook: Data and Applications Security and Privacy XXXVII; 37th Annual IFIP WG Vijayalakshmi Atluri,Anna Lisa Ferrara Conference proceedings 2023

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Impact of?Using a?Privacy Model on?Smart Buildings Data for?CO, Predictionor worse than others; also, the temporal dimension was particularly sensitive, with scores decreasing up to . between the original and the transformed data. This shows the effect of different levels of data privacy on the data utility of IoT applications, and can also help to identify which paramete

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Digital Twins for?IoT Security Managementof concept to demonstrate the practical applicability of this approach for four different security use cases. Our results provide a starting point for further research to leverage digital twins for IoT security management.

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Data Distribution Impact on?Preserving Privacy in?Centralized and?Decentralized Learning Learning (DILDP-FL). DILDP-FL is based on the distribution-invariant privatization method known as DIP. It transforms and perturbs the data while employing suitable transformations to achieve query results similar to those obtained from the original data. Our experimental findings demonstrate that

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FECT

Cytology

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Data and Applications Security and Privacy XXXVII978-3-031-37586-6Series ISSN 0302-9743 Series E-ISSN 1611-3349

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https://doi.org/10.1007/978-3-540-47590-3 With LDP, users can perturb their data on their devices before sending it out for analysis. However, as the collection of multiple sensitive information becomes more prevalent across various industries, collecting a single sensitive attribute under LDP may not be sufficient. Correlated attributes i

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Japan Association for‘Chemical Innovationesence information of a particular individual could be revealed from the statistics obtained in large-scale genomic analyses. Existing methods for releasing genome statistics under differential privacy do not prevent the leakage of personal information by untrusted data collectors. In addition, the