Welcome to the resource topic for 2023/1203
Collaborative Privacy-Preserving Analysis of Oncological Data using Multiparty Homomorphic Encryption
Authors: Ravit Geva, Alexander Gusev, Yuriy Polyakov, Lior Liram, Oded Rosolio, Andreea Alexandru, Nicholas Genise, Marcelo Blatt, Zohar Duchin, Barliz Waissengrin, Dan Mirelman, Felix Bukstein, Deborah T. Blumenthal, Ido Wolf, Sharon Pelles-Avraham, Tali Schaffer, Lee A. Lavi, Daniele Micciancio, Vinod Vaikuntanathan, Ahmad Al Badawi, Shafi GoldwasserAbstract:
Real-world healthcare data sharing is instrumental in constructing broader-based and larger clinical data sets that may improve clinical decision-making research and outcomes. Stakeholders are frequently reluctant to share their data without guaranteed patient privacy, proper protection of their data sets, and control over the usage of their data. Fully homomorphic encryption (FHE) is a cryptographic capability that can address these issues by enabling computation on encrypted data without intermediate decryptions, so the analytics results are obtained without revealing the raw data. This work presents a toolset for collaborative privacy-preserving analysis of oncological data using multiparty FHE. Our toolset supports survival analysis, logistic regression training, and several common descriptive statistics. We demonstrate using oncological data sets that the toolset achieves high accuracy and practical performance, which scales well to larger data sets. As part of this work, we propose a novel cryptographic protocol for interactive bootstrapping in multiparty FHE, which is of independent interest. The toolset we develop is general-purpose and can be applied to other collaborative medical and healthcare application domains.
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