Securely Aggregating Testimonies with Threshold Multi-key FHE

  • Gerald GavinEmail author
  • Stephane Bonnevay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11445)


Many data management applications, such as setting up Web portals, managing enterprise data, managing community data, and sharing scientific data, require integrating data from multiple sources. Each of these sources provides a set of values and different sources can often provide conflicting values. To discover the true values, data integration systems should resolve conflicts. In this paper, we present a formal probabilistic framework in the expert/authority setting. Each expert has a partial and maybe imperfect view of a binary target vector \(\varvec{b}\) that an authority wishes recovering. The goal of this paper consists of proposing a multi-party aggregating function of experts’ views to recover \(\varvec{b}\) with an error rate as small as possible. In addition, it is assumed that some of the experts are corrupted by an adversary \(\mathcal {A}\). This adversary controls and coordinates the behavior of the corrupted experts and can thus perturb the aggregating process. In this paper, we present a simple aggregating function and we provide a formal upper-bound over of the output vector error expectation in the worst case, i.e. whatever the behavior of the adversary is. We then propose to securely implement this aggregating function in order to preserve the privacy of experts’ views. A natural secure implementation could be achieved with recent powerful cryptographic tools, i.e. Threshold Multi-key Fully Homomorphic Encryptions schemes (TMFHE). Finally, trade-off between the time complexity and the number of interaction rounds are proposed.



The authors would like to thank the BAG members for their helpful discussions always around a coffee.


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Authors and Affiliations

  1. 1.Laboratory ERICUniversity of LyonLyonFrance

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