Verifiable secret sharing

About: Verifiable secret sharing is a research topic. Over the lifetime, 4241 publications have been published within this topic receiving 99569 citations.

Dynamic quantum secret sharing protocol based on GHZ state

Abstract: This work proposes a new dynamic quantum secret sharing (DQSS) protocol using the measurement property of Greenberger---Horne---Zeilinger state and the controlled-NOT gate. In the proposed DQSS protocol, an agent can obtain a shadow of the secret key by simply performing a measurement on single photons. In comparison with the existing DQSS protocols, it provides better qubit efficiency and has an easy way to add a new agent. The proposed protocol is also free from the eavesdropping attack, the collusion attack, and can have an honesty check on a revoked agent.

Verifiable shuffle of large size ciphertexts

Abstract: A shuffle is a permutation and rerandomization of a set of ciphertexts. Among other things, it can be used to construct mix-nets that are used in anonymization protocols and voting schemes. While shuffling is easy, it is hard for an outsider to verify that a shuffle has been performed correctly. We suggest two efficient honest verifier zero-knowledge (HVZK) arguments for correctness of a shuffle. Our goal is to minimize round-complexity and at the same time have low communicational and computational complexity. The two schemes we suggest are both 3-move HVZK arguments for correctness of a shuffle. We first suggest a HVZK argument based on homomorphic integer commitments, and improve both on round complexity, communication complexity and computational complexity in comparison with state of the art. The second HVZK argument is based on homomorphic commitments over finite fields. Here we improve on the computational complexity and communication complexity when shuffling large ciphertexts.

P4P: practical large-scale privacy-preserving distributed computation robust against malicious users

Abstract: In this paper we introduce a framework for privacy-preserving distributed computation that is practical for many real-world applications. The framework is called Peers for Privacy (P4P) and features a novel heterogeneous architecture and a number of efficient tools for performing private computation and ensuring security at large scale. It maintains the following properties: (1) Provably strong privacy; (2) Adequate efficiency at reasonably large scale; and (3) Robustness against realistic adversaries. The framework gains its practicality by decomposing data mining algorithms into a sequence of vector addition steps that can be privately evaluated using a new verifiable secret sharing (VSS) scheme over small field (e.g., 32 or 64 bits), which has the same cost as regular, non-private arithmetic. This paradigm supports a large number of statistical learning algorithms including SVD, PCA, k-means, ID3, EM-based machine learning algorithms, etc., and all algorithms in the statistical query model [36]. As a concrete example, we show how singular value decomposition (SVD), which is an extremely useful algorithm and the core of many data mining tasks, can be done efficiently with privacy in P4P. Using real-world data and actual implementation we demonstrate that P4P is orders of magnitude faster than existing solutions.