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The classical distributed key generation protocols (DKG) are resurging due to their widespread applications in blockchain. While efforts have been made to improve DKG communication, practical large-scale deployments are still yet to come due to various challenges, including the heavy computation and communication (particularly broadcast) overhead in their adversarial cases. In this paper, we propose a practical DKG for DLog-based cryptosystems, which achieves (quasi-)linear computation and communication per-node cost with the help of a common coin, even in the face of the maximal amount of Byzantine nodes. Moreover, our protocol is secure against adaptive adversaries, which can corrupt less than half of all nodes. The key to our improvements lies in delegating the most costly operations to an Any-Trust group together with a set of techniques for adaptive security. This group is randomly sampled and consists of a small number of individuals. The population only trusts that at least one member in the group is honest, without knowing which one. Moreover, we present a generic transformer that enables us to efficiently deploy a conventional distributed protocol like our DKG, even when the participants have different weights. Additionally, we introduce an extended broadcast channel based on a blockchain and data dispersal network (such as IPFS), enabling reliable broadcasting of arbitrary-size messages at the cost of constant-size blockchain storage., Comment: 22 pages, 5 figures

Vertical Federated Learning (FL) is a new paradigm that enables users with non-overlapping attributes of the same data samples to jointly train a model without directly sharing the raw data. Nevertheless, recent works show that it's still not sufficient to prevent privacy leakage from the training process or the trained model. This paper focuses on studying the privacy-preserving tree boosting algorithms under the vertical FL. The existing solutions based on cryptography involve heavy computation and communication overhead and are vulnerable to inference attacks. Although the solution based on Local Differential Privacy (LDP) addresses the above problems, it leads to the low accuracy of the trained model. This paper explores to improve the accuracy of the widely deployed tree boosting algorithms satisfying differential privacy under vertical FL. Specifically, we introduce a framework called OpBoost. Three order-preserving desensitization algorithms satisfying a variant of LDP called distance-based LDP (dLDP) are designed to desensitize the training data. In particular, we optimize the dLDP definition and study efficient sampling distributions to further improve the accuracy and efficiency of the proposed algorithms. The proposed algorithms provide a trade-off between the privacy of pairs with large distance and the utility of desensitized values. Comprehensive evaluations show that OpBoost has a better performance on prediction accuracy of trained models compared with existing LDP approaches on reasonable settings. Our code is open source.

We consider the charged d-dimensional black holes in a cavity in extended phase space and investigate the heat engine and the Joule-Thomson (JT) expansion. Since the phase structure of black holes in a cavity is similar to anti-de-sitter (AdS) cases, we take black holes in a cavity as the working substance in the heat engine and calculate their efficiency in Carnot cycle and rectangular cycle. Also, we discuss whether the JT expansion of charged black holes in a cavity is consistent with AdS cases and conclude the effect of different boundary conditions on black hole thermodynamics.

We investigate the thermodynamical properties of charged torus-like black holes and take it as the working substance to study the heat engines. In the extended phase space, by interpreting the cosmological constant as the thermodynamic pressure, we derive the thermodynamical quantities by the first law of black hole thermodynamics and obtain the equation of state. Then, we calculate the efficiency of the heat engine in Carnot cycle as well as rectangular cycle, and investigate how the efficiency changes with respect to volume. In addition, to avoid a negative temperature, we emphasize that the charge of this black hole can not be arbitrary. Last, we check the calculation accuracy of a benchmark scheme and discuss the upper bound and lower bound for charged torus-like black hole in the scheme., Comment: 16 pages, 8 figures, accepted by Communications in Theoretical Physics

In this paper, we study the Joule-Thomson expansion for RN-AdS black holes immersed in perfect fluid dark matter. Firstly, the negative cosmological constant could be interpreted as thermodynamic pressure and its conjugate quantity with the volume gave us more physical insights into the black hole. We derive the thermodynamic definitions and study the critical behaviour of this black hole. Secondly, the explicit expression of Joule-Thomson coefficient is obtained from the basic formulas of enthalpy and temperature. Then, we obtain the isenthalpic curve in $T-P$ graph and demonstrate the cooling-heating region by the inversion curve. At last, we derive the ratio of minimum inversion temperature to critical temperature and the inversion curves in terms of charge $Q$ and parameter $\lambda$.

The shuffle model is recently proposed to address the issue of severe utility loss in Local Differential Privacy (LDP) due to distributed data randomization.In the shuffle model, a shuffler is utilized to break the link between the user identity and the message uploaded to the data analyst. Since less noise needs to be introduced to achieve the same privacy guarantee, following this paradigm, the utility of privacy-preserving data collection is improved. We propose DUMP (\underline{DUM}my-\underline{P}oint-based), a framework for privacy-preserving histogram estimation in the shuffle model. The core of DUMP is a new concept of \emph{dummy blanket}, which enables enhancing privacy by just introducing \textit{points }on the user side and further improving the utility of the shuffle model.We instantiate DUMP by proposing two protocols: pureDUMP and mixDUMP, and conduct a comprehensive experimental evaluation to compare them with existing protocols. The experimental results show that, under the same privacy guarantee, (1) the proposed protocols have significant improvements in communication efficiency over all existing multi-message protocols, by at least 3 orders of magnitude; (2) they achieve competitive utility, while the only known protocol (Ghazi \textit{et al.}, PMLR 2020) having better utility than ours employs hard-to-exactly-sample distributions which are vulnerable to floating-point attacks (CCS 2012).