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- W4378473864 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." @default.
- W4378473864 created "2023-05-27" @default.
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- W4378473864 date "2023-05-24" @default.
- W4378473864 modified "2023-09-23" @default.
- W4378473864 title "Confidential Truth Finding with Multi-Party Computation (Extended Version)" @default.
- W4378473864 doi "https://doi.org/10.48550/arxiv.2305.14727" @default.
- W4378473864 hasPublicationYear "2023" @default.
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