# Fraud-Proof Revenue Division on Subscription Platforms

arXiv: https://arxiv.org/abs/2511.04465 · HTML: https://mlsanity.com/paper/2511.04465/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 50 references that are in the map.
- First posted on arXiv 6 Nov 2025.

## Builds on (most cited first, 9 of 9)

- [Dynamic Incentive-aware Learning: Robust Pricing in Contextual Auctions](https://mlsanity.com/paper/2002.11137/) (2020)
- [How Bad is Top-K Recommendation under Competing Content Creators?](https://mlsanity.com/paper/2302.01971/) (2023)
- [Learning Product Rankings Robust to Fake Users](https://mlsanity.com/paper/2009.05138/) (2020)
- [Performative Recommendation: Diversifying Content via Strategic Incentives](https://mlsanity.com/paper/2302.04336/) (2023)
- [Truthful Aggregation of Budget Proposals](https://mlsanity.com/paper/1905.00457/) (2019)
- [Minimizing the Union: Tight Approximations for Small Set Bipartite Vertex Expansion](https://mlsanity.com/paper/1611.07866/) (2016)
- [Settling the Score: Portioning with Cardinal Preferences](https://mlsanity.com/paper/2307.15586/) (2023)
- [Abuse and Fraud Detection in Streaming Services Using Heuristic-Aware Machine Learning](https://mlsanity.com/paper/2203.02124/) (2022)
- [The Shapley index for music streaming platforms](https://mlsanity.com/paper/2411.07166/) (2024)

## Abstract (arXiv)

We study a model of subscription-based platforms where users pay a fixed fee for unlimited access to content, and creators receive a share of the revenue. Existing approaches to detecting fraud predominantly rely on machine learning methods, engaging in an ongoing arms race with bad actors. We explore revenue division mechanisms that inherently disincentivize manipulation. We formalize three types of manipulation-resistance axioms and examine which existing rules satisfy these. We show that a mechanism widely used by streaming platforms, not only fails to prevent fraud, but also makes detecting manipulation computationally intractable. We also introduce a novel rule, ScaledUserProp, that satisfies all three manipulation-resistance axioms. Finally, experiments with both real-world and synthetic streaming data support ScaledUserProp as a fairer alternative compared to existing rules.