# A Critical Overview of Privacy-Preserving Approaches for Collaborative Forecasting

arXiv: https://arxiv.org/abs/2004.09612 · HTML: https://mlsanity.com/paper/2004.09612/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2020.

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

- [Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2211.02674/) (2022)

## Abstract (arXiv)

Cooperation between different data owners may lead to an improvement in forecast quality - for instance by benefiting from spatial-temporal dependencies in geographically distributed time series. Due to business competitive factors and personal data protection questions, said data owners might be unwilling to share their data, which increases the interest in collaborative privacy-preserving forecasting. This paper analyses the state-of-the-art and unveils several shortcomings of existing methods in guaranteeing data privacy when employing Vector Autoregressive (VAR) models. The paper also provides mathematical proofs and numerical analysis to evaluate existing privacy-preserving methods, dividing them into three groups: data transformation, secure multi-party computations, and decomposition methods. The analysis shows that state-of-the-art techniques have limitations in preserving data privacy, such as a trade-off between privacy and forecasting accuracy, while the original data in iterative model fitting processes, in which intermediate results are shared, can be inferred after some iterations.