Backend
GRIDSHIELD: Verifiable Grid Feasibility and Manipulation Resistance in Privacy-Preserving Peer-to-Peer Energy Trading
arXiv cs.CR0 views
arXiv:2611.05816v1 Announce Type: new Abstract: Blockchain-based peer-to-peer (P2P) energy markets let neighbors trade directly, but concentrate unchecked power in the clearing operator. Most prior work addresses only part of this: network-aware clearing respects grid constraints but operates on plaintext allocations, while privacy-preserving designs hide bids yet either drop the network constraint, enforce it only inside an unauditable computation, or recover it at the cost of a per-circuit trusted setup. Neither considers what happens if an adversary captures the operator's clearing privilege to reorder trades for profit or curtail a rival. We present GRIDSHIELD, a P2P energy-trading system that clears sealed bids with an order-independent batch program, removing the welfare a compromised operator can extract by reordering trades, and proves the result grid-feasible via an aggregated congestion-feasibility proof (ACFP). Exploiting the additive homomorphism of Pedersen commitments, ACFP reconstructs each grid constraint directly from per-bus commitments, reducing feeder-wide feasibility verification over any public grid constraints to a single aggregated range proof over the hidden allocation, with a reveal-and-recompute challenge that catches feasible yet unfair clearings. We introduce congestion-responsibility fairness (CRF), a metric scoring curtailment against each party's physical responsibility for congestion rather than consumption. We evaluate GRIDSHIELD against three prior P2P energy-trading designs across four feeders, including a 240-node Midwest feeder with real hourly loads, on a live Hyperledger Fabric network benchmarked with Hyperledger Caliper. GRIDSHIELD proves an entire round feasible with a few-kilobyte proof and no trusted setup, rejects infeasible allocations two of the three compared designs accept, and sustains $\approx$1064 tx/s.
Source:https://arxiv.org/abs/2611.05816PreviousWhen File Ingestion Misleads Language Models: Measuring and Mitigating Divergences in Office DocumentsNextA Hybrid Approach for Automated Security Pattern Recovery
Related posts
Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data
0arXiv stat.ML
When the Agent Learns Too: Learning to Incentivize with Unobserved Rewards
0arXiv stat.ML
Probabilistic Meshless Methods on Manifolds
0arXiv stat.ML
Leveraging Soft Prompts for Privacy Attacks in Federated Prompt Tuning
0arXiv cs.CR
When Relationships Break: Interpreting Network Traffic Anomalies via Dependency Violations
0arXiv cs.CR
GhostTac: Manipulating Tactile Sensors without Physical Contact
0arXiv cs.CR