# When Privacy Moves ML-Mediated Decisions On Device: Information and Incentive Misalignment in Auctions

arXiv: https://arxiv.org/abs/2609.33312 · HTML: https://mlsanity.com/paper/2609.33312/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 27 Sep 2026.

## Builds on (most cited first, 10 of 10)

- [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025)
- [WildChat: 1M ChatGPT Interaction Logs in the Wild](https://mlsanity.com/paper/2405.01470/) (2024)
- [Online Learning under Delayed Feedback](https://mlsanity.com/paper/1306.0686/) (2013)
- [The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems](https://mlsanity.com/paper/2011.10124/) (2020)
- [Real-Time Bidding Benchmarking with iPinYou Dataset](https://mlsanity.com/paper/1407.7073/) (2014)
- [Budget Pacing in Repeated Auctions: Regret and Efficiency without Convergence](https://mlsanity.com/paper/2205.08674/) (2022)
- [Multiplicative Pacing Equilibria in Auction Markets](https://mlsanity.com/paper/1706.07151/) (2017)
- [Smart Pacing for Effective Online Ad Campaign Optimization](https://mlsanity.com/paper/1506.05851/) (2015)
- [Real Time Bid Optimization with Smooth Budget Delivery in Online Advertising](https://mlsanity.com/paper/1305.3011/) (2013)
- [A Field Guide for Pacing Budget and ROS Constraints](https://mlsanity.com/paper/2302.08530/) (2023)

## Abstract (arXiv)

Moving ML-mediated decision making onto privacy-preserving clients decentralises the economic decision along with the inference. Shared budget constraints then depend on information that cannot be globally current, creating an information-structure failure that conventional pacing is not designed to solve. We study this information misalignment in an auction-logic-faithful on-device simulation with 36 campaigns and 50 devices. Accounting is in dimensionless integer score units; no currency semantics are claimed. Across 30 paired demand paths, proportional Even pacing overspends 17.77% after one tick of staleness and 1,669.31% after 50 ticks under the original 20-times budget pressure. The effect does not depend on that severe a budget: at two-times pressure, 50-tick overspend remains 106.95%. A visible-budget no-sale guard makes zero-lag compliance exact at this score-unit granularity, yet leaves 11.88% overspend at one tick because other devices' debits remain invisible. A declared bursty, heterogeneous-device sweep retains a strictly increasing mean lag curve. We derive a finite-window expected excess-debit bound under conditional charge caps and find positive paired slack in every bounded-value cell. A second, incentive misalignment arises when the ML/pacing score transformation is allowed to change payment units: 98.23% of rival auctions at one tick admit a profitable deviation. An executable implementation-level counterexample isolates the runner-up's multiplier in the winner's price. Critical-base-bid payment is per-auction DSIC conditional on current multipliers, but does not establish dynamic truthfulness and does not repair base-value ranking disagreement.