Privacy-Preserving and Approximately Truthful Local Electricity Markets: A Differentially Private VCG Mechanism
Bibliographic record
Abstract
Privacy-aware market participants care about the leakage of their private information via statistical releases of local electricity markets outputs. This kind of privacy breach would have major implications on the future transactions of the market participants and unauthorized observers’ beliefs about them. To address this challenge, we introduce the notion of noisy electricity markets in the framework of Differential Privacy (DP) for preserving the privacy of individuals and maintaining the utility of their data for social good. In this regard, this paper proposes a novel differentially private mechanism for local electricity markets that releases a near-optimal solution while guarantying the outputs of the market would reveal almost nothing about any individual’s input data. To do so, we implement the exponential mechanism for privatizing the baseline Vickrey–Clarke–Groves (VCG) mechanism in the proposed local electricity market. Moreover, we provide an upper-bound on the social welfare loss incurred by the privacy constraint and analyze the inherent trade-off between the privacy and suboptimality. In the end, numerical case studies for reflecting the theoretical properties of the proposed mechanism are provided.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.014 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".