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PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
The Blocklace: A Byzantine-repelling and Universal Confli...
Paulo Sérgio Almeida, Ehud Shapiro · 2024-02-13 · via cs.DS updates on arXiv.org

Conflict-free Replicated Data Types (CRDTs) are designed for replica convergence without global coordination or consensus. Recent work has achieved the same in a Byzantine environment, through DAG-like structures based on cryptographic hashes of content. The blocklace is a partially-ordered generalization of the blockchain in which each block has any finite number of signed hash pointers to preceding blocks. We show that the blocklace datatype, with the sole operation of adding a single block, is a CRDT: it is both a pure operation-based CRDT, with self-tagging; and a delta-state CRDT, under a slight generalization of the delta framework. Allowing arbitrary values as payload, the blocklace can also be seen as a universal Byzantine fault-tolerant implementation for arbitrary CRDTs, under the operation-based approach. Current approaches only care about CRDT convergence, being equivocation-tolerant (they do not detect or prevent equivocations), allowing a Byzantine node to cause an arbitrary amount of harm by polluting the CRDT state with an unbounded number of equivocations. We show that the blocklace can be used not only in an equivocation-tolerant way, but also so as to detect and eventually exclude Byzantine nodes, including equivocators, even under the presence of undetectable colluders. The blocklace CRDT protocol ensures that a Byzantine node may harm only a finite prefix of the computation.