# Privacy-Aware Data Acquisition under Data Similarity in Regression Markets

arXiv: https://arxiv.org/abs/2312.02611 · HTML: https://mlsanity.com/paper/2312.02611/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 45 references that are in the map.
- First posted on arXiv 5 Dec 2023.

## Builds on (most cited first, 20 of 20)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [A Marketplace for Data: An Algorithmic Solution](https://mlsanity.com/paper/1805.08125/) (2018)
- [Energy Demand Prediction with Federated Learning for Electric Vehicle Networks](https://mlsanity.com/paper/1909.00907/) (2019)
- [A Crowdsourcing Framework for On-Device Federated Learning](https://mlsanity.com/paper/1911.01046/) (2019)
- [Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms](https://mlsanity.com/paper/2201.03968/) (2022)
- [Joint Service Pricing and Cooperative Relay Communication for Federated Learning](https://mlsanity.com/paper/1811.12082/) (2018)
- [Truthful Linear Regression](https://mlsanity.com/paper/1506.03489/) (2015)
- [Optimal Data Acquisition with Privacy-Aware Agents](https://mlsanity.com/paper/2209.06340/) (2022)
- [Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey](https://mlsanity.com/paper/1608.03475/) (2016)
- [Regression markets and application to energy forecasting](https://mlsanity.com/paper/2110.03633/) (2021)
- [FedToken: Tokenized Incentives for Data Contribution in Federated Learning](https://mlsanity.com/paper/2209.09775/) (2022)
- [A Marketplace for Trading AI Models based on Blockchain and Incentives for IoT Data](https://mlsanity.com/paper/2112.02870/) (2021)
- [Market Model and Optimal Pricing Scheme of Big Data and Internet of Things (IoT)](https://mlsanity.com/paper/1602.03202/) (2016)
- [Internet of Things (IoT) Connectivity in 6G: An Interplay of Time, Space, Intelligence, and Value](https://mlsanity.com/paper/2111.05811/) (2021)
- [Semi-Private Computation of Data Similarity with Applications to Data Valuation and Pricing](https://mlsanity.com/paper/2206.06650/) (2022)
- [Strategic Coalition for Data Pricing in IoT Data Markets](https://mlsanity.com/paper/2206.07785/) (2022)
- [Bayesian Regression Markets](https://mlsanity.com/paper/2310.14992/) (2023)
- [Mobile Data Trading: Behavioral Economics Analysis and Algorithm Design](https://mlsanity.com/paper/1702.02722/) (2017)

## Built on by (most cited first, 1 of 1)

- [Managing Correlations in Data and Privacy Demand](https://mlsanity.com/paper/2509.02856/) (2025)

## Abstract (arXiv)

Data markets facilitate decentralized data exchange for applications such as prediction, learning, or inference. The design of these markets is challenged by varying privacy preferences as well as data similarity among data owners. Related works have often overlooked how data similarity impacts pricing and data value through statistical information leakage. We demonstrate that data similarity and privacy preferences are integral to market design and propose a query-response protocol using local differential privacy for a two-party data acquisition mechanism. In our regression data market model, we analyze strategic interactions between privacy-aware owners and the learner as a Stackelberg game over the asked price and privacy factor. Finally, we numerically evaluate how data similarity affects market participation and traded data value.