A critical overview of privacy-preserving approaches for collaborative forecasting

Carla Gonçalves, Ricardo J. Bessa*, Pierre Pinson

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Cooperation between different data owners may lead to an improvement in forecast quality—for instance, by benefiting from spatiotemporal dependencies in geographically distributed time series. Due to business competitive factors and personal data protection concerns, however, said data owners might be unwilling to share their data. Interest in collaborative privacy-preserving forecasting is thus increasing. This paper analyzes the state-of-the-art and unveils several shortcomings of existing methods in guaranteeing data privacy when employing vector autoregressive models. The methods are divided 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 (i) the necessary trade-off between privacy and forecasting accuracy, empirically evaluated through simulations and real-world experiments based on solar data; and (ii) iterative model fitting processes, which reveal data after a number of iterations.

Original languageEnglish
JournalInternational Journal of Forecasting
Volume37
Issue number1
Pages (from-to)322-342
ISSN0169-2070
DOIs
Publication statusPublished - 1 Jan 2020

Keywords

  • ADMM
  • Forecasting
  • Privacy-preserving
  • Time series
  • Vector autoregression

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