Cost Parameter Recovery Attack via Exploitation of Real-time Price Signals in Power System
Bibliographic record
Abstract
To promote transparency, monitor emissions, and help market participants make more informed decisions in smart grids, Independent System Operators (ISOs) and governmental agencies often publish market data, including prices and power generation levels. For example, the ISO of New England provides a real-time Locational Marginal Price (LMP) map and aggregates power generation data by resource type every five minutes. LMPs—which reflect real-time price signals at different locations in the market—are typically derived as the Lagrange multipliers of the AC Optimal Power Flow (ACOPF) problem, which is solved at regular intervals. As outputs of ACOPF, both power generation levels and LMPs are highly valuable, and are frequently used in research to support fast decision-making processes. Although this information is not inherently sensitive, adversaries may exploit these datasets to infer private details about the power grid, such as generators’ cost functions. In this paper, we demonstrate that adversaries can accurately recover the coefficients of each generator’s cost function by combining two key pieces of publicly available information—power generation levels and LMPs—with a small amount of data from the targeted bus.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".