Accessibility of covariance information creates vulnerability in Federated Learning frameworks.

MOTIVATION: Federated Learning (FL) is gaining traction in various fields as it enables integrative data analysis without sharing sensitive data, such as in healthcare. However, the risk of data leakage caused by malicious attacks must be considered. In this study, we introduce a novel attack algorithm that relies on being able to compute sample means, sample covariances, and construct known linearly independent vectors on the data owner side. RESULTS: We show that these basic functionalities, which are available in several established FL frameworks, are sufficient to reconstruct privacy-protected data. Additionally, the attack algorithm is robust to defense strategies that involve adding random noise. We demonstrate the limitations of existing frameworks and propose potential defense strategies analyzing the implications of using differential privacy. The novel insights presented in this study will aid in the improvement of FL frameworks. AVAILABILITY AND IMPLEMENTATION: The code examples are provided at GitHub (https://github.com/manuhuth/Data-Leakage-From-Covariances.git). The CNSIM1 dataset, which we used in the manuscript, is available within the DSData R package (https://github.com/datashield/DSData/tree/main/data).

Anestesia dos nervos alveolar inferior e lingual / Anesthesia of alveolar inferior and lingual nerves

Foram anestesiados 57 pacientes pertencentes aos grupos étnicos leucodermas e melanodermas, nas Clínicas da Faculdade de Odontologia de Piracicaba, com idades que variavam de 12 a 52 anos, preconizando a técnica intra-oral dos nervos alveolar inferior e lingual de Archer e Monhein, por nós modificada. Esta técnica foi coroada de êxito absoluto, quer seja o paciente desdentado total, desdentado posterior, dentado total ou dentado posterior