Year: 2023
Type: preprint
Abstract: Federated knowledge discovery and data mining are challenged to assess the trustworthiness of data originating from autonomous sources while protecting confidentiality and privacy. Truth-finding algorithms help corroborate data from disagreeing sources. For each query it receives, a truth-finding algorithm predicts a truth value of the answer, possibly updating the trustworthiness factor of each source. Few works, however, address the issues of confidentiality and privacy. We devise and present a secure secret-sharing-based multi-party computation protocol for pseudo-equality tests that are used in truth-finding algorithms to compute additions depending on a condition. The protocol guarantees confidentiality of the data and privacy of the sources. We also present variants of truth-finding algorithms that would make the computation faster when executed using secure multi-party computation. We empirically evaluate the performance of the proposed protocol on two state-of-the-art truth-finding algorithms, Cosine, and 3-Estimates, and compare them with that of the baseline plain algorithms. The results confirm that the secret-sharing-based secure multi-party algorithms are as accurate as the corresponding baselines but for proposed numerical approximations that significantly reduce the efficiency loss incurred.
Source: arXiv (Cornell University)
Institutions: Télécom Paris, Centre Inria de Saclay, Laboratoire d'Informatique de l'École Polytechnique, Institut Polytechnique de Paris, Laboratoire Traitement et Communication de l’Information, CNRS@CREATE Ltd (Singapore), GRACE: Geometry, arithmetic, algorithms, codes and encryption, Institut Universitaire de France, École Normale Supérieure - PSL, Institute for Infocomm Research, Image and Pervasive Access Laboratory, Département d'Informatique, VALDA: Valeur à partir des données, National University of Singapore
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: National Research Foundation
OpenAlex ID: W4378473864