Distributed common randomness generation (i.e., the common coin problem) is a cornerstone of randomized distributed computing. While a long line of research has sought scalable solutions, the asynchronous setting remains a challenge. Specifically, while Blum et al. (TCC'21) achieved sub-quadratic communication complexity, their approach lacks “balance”: certain nodes must still send Ω(n) messages, creating a scalability bottleneck. Furthermore, their solution only tolerates a 1/3 - ε fraction of corrupted nodes, whereas the classic construction by Cachin et al. (PODC'00) tolerates up to 1/2 under the same setup assumptions.

Multi-valued Validated Byzantine Agreement (MVBA) is vital for asynchronous distributed protocols like asynchronous BFT consensus and distributed key generation, making performance improvements a long-standing goal. Existing communication-optimal MVBA protocols rely on computationally intensive public-key cryptographic tools, such as non-interactive threshold signatures, which are also vulnerable to quantum attacks. While hash-based MVBA protocols have been proposed to address these challenges, their higher communication overhead has raised concerns about practical performance. We present a novel MVBA protocol with adaptive security, relying exclusively on hash functions to achieve post-quantum security. Our protocol delivers near-optimal communication, constant round complexity, and significantly reduced latency compared to existing schemes, though it has sub-optimal resilience, tolerating up to 20% Byzantine corruptions instead of the typical 33%. For example, with n = 201 and input size 1.75 MB, it reduces latency by 81% over previous hash-based approaches.

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 overhead (particularly broadcast) in 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. 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.

Several distributed protocols, including distributed key generation (DKG) and interactive consistency (IC), depend on O(n) instances of Byzantine Broadcast or Byzantine Agreement among n nodes, resulting in Θ(n3) communication overhead.

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 (DUMmy-Point-based), a framework for privacy-preserving histogram estimation in the shuffle model. The core of DUMP is a new concept ofdummy blanket, which enables enhancing privacy by just introducing dummy 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 (Ghaziet al., PMLR 2020) having better utility than ours employs hard-to-exactly-sample distributions which are vulnerable to floating-point attacks (CCS 2012).

Many data applications can be facilitated or even spawned by joint analysis on databases held by different owners, but privacy concerns are currently the biggest hindrance. Though a practical privacy-preserving collaborative database analytics system is strongly desired, existing approaches do not support efficient queries for several essential SQL operators such as the general join, especially on large databases. In this paper, we propose, analyze, and implement Scape, a Scalable Collaborative Analytics system on Private databasE with malicious security. In Scape, databases from different parties are secretly shared to three non-colluding computing parties. Users can perform various SQL queries (including fully functional Join, Group by, Aggregation, etc.) on shared databases, and all entities learn nothing beyond their priori knowledge during the whole execution even when they deviate from protocols. At the heart of Scape lies several asymptotically efficient SQL protocols. Particularly, our general join protocol has O (n log2n + m) communication/computation cost when joining two tables with o (n) rows to a table with 0 (m) rows, significantly outperforming the state-of-the-art approach with O(n2) cost. The benchmark results confirm the advantages of Scape, which is up to 25 x faster than the baseline.

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.

Summary As the value of data has received considerable attention, data trading shows broad market prospects. The existing data trading methods, including private trades and centralized trades, have high risks regarding transaction security and data protection. To solve this problem, we propose a decentralized trading solution for open fair data trading by deploying the smart contract on the blockchain network. The data for sale are encrypted and stored on the distributed storage platform but not directly on the blockchain network. Because the trading content is the decryption key of the data, the proposed new method can alleviate the storage pressure of the blockchain by reducing the transaction cost. We conduct a security analysis which shows that our scheme achieves secure, practical, open, and fair trading. We implement our trading contract with solidity and test it on the Ethereum's test network, and extensive experiments demonstrate desirable feasibility of our proposal.