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Conference Papers Year : 2023

Confidential Truth Finding with Multi-Party Computation


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 a variants of a truth-finding algorithm that would make the computation faster when executed using secure multi-party computation. We empirically evaluate the performance of the proposed protocol on a state-of-the-art truth-finding algorithm, 3-Estimates, and compare it with that of the baseline plain algorithm. 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.
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hal-04139281 , version 1 (23-06-2023)



  • HAL Id : hal-04139281 , version 1


Angelo Saadeh, Pierre Senellart, Stéphane Bressan. Confidential Truth Finding with Multi-Party Computation. DEXA 2023 - 34th International Conference on Database and Expert Systems Applications, Aug 2023, Penang, Malaysia. ⟨hal-04139281⟩
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