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Theoretical Limits of Language Model Alignment $f$-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models When Can Voting Help, Hurt, or Change Course? Exact Structure of Binary Test-Time Aggregation When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation Conditional Diffusion Under Linear Constraints: Langevin Mixing and Information-Theoretic Guarantees Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Expert Routing for Communication-Efficient MoE via Finite Expert Banks Contextual Memory-Enhanced Source Coding for Low-SNR Communications Realizable Bayes-Consistency for General Metric Losses Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy Optimization of CV-QKD Under Practical Constraints Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness Real-Time Text Transmission via LLM-Based Entropy Coding over Fixed-Rate Channels SwiftChannel: Algorithm-Hardware Co-Design for Deep Learning-Based 5G Channel Estimation Evolving Token Communication with Parametric Memory Network Remote Action Generation: Remote Control with Minimal Communication The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation Linear-Readout Floors and Threshold Recovery in Computation in Superposition Soft Graph Diffusion Transformer for MIMO Detection Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design Exponential families from a single KL identity MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness Diffusion-OAMP for Joint Image Compression and Wireless Transmission Decoupled Descent: Exact Test Error Tracking Via Approximate Message Passing
SeF: A Secure Fountain Architecture for Slashing Storage ...
Swanand Kadhe, Jichan Chung, Kannan Ramchandran · 2019-06-28 · via cs.IT updates on arXiv.org

Full nodes, which synchronize the entire blockchain history and independently validate all the blocks, form the backbone of any blockchain network by playing a vital role in ensuring security properties. On the other hand, a user running a full node needs to pay a heavy price in terms of storage costs. E.g., the Bitcoin blockchain size has grown over 215GB, in spite of its low throughput. The ledger size for a high throughput blockchain Ripple has already reached 9TB, and it is growing at an astonishing rate of 12GB per day! In this paper, we propose an architecture based on 'fountain codes', a class of erasure codes, that enables any full node to 'encode' validated blocks into a small number of 'coded blocks', thereby reducing its storage costs by orders of magnitude. In particular, our proposed "Secure Fountain (SeF)" architecture can achieve a near-optimal trade-off between the storage savings per node and the 'bootstrap cost' in terms of the number of (honest) storage-constrained nodes a new node needs to contact to recover the blockchain. A key technical innovation in SeF codes is to make fountain codes secure against adversarial nodes that can provide maliciously formed coded blocks. Our idea is to use the header-chain as a 'side-information' to check whether a coded block is maliciously formed while it is getting decoded. Further, the 'rateless property' of fountain codes helps in achieving high decentralization and scalability. Our experiments demonstrate that SeF codes tuned to achieve 1000x storage savings enable full nodes to encode the 191GB Bitcoin blockchain into 195MB on average. A new node can recover the blockchain from an arbitrary set of storage-constrained nodes as long as the set contains ~1100 honest nodes on average. Note that for a 1000x storage savings, the fundamental bound on the number of honest nodes to contact is 1000: we need about 10% more in practice.