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Machine learning models in trusted research environments - understanding operational risks.

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Summary
This summary is machine-generated.

Trusted research environments (TREs) face new disclosure risks from machine learning (ML) models. Understanding these novel risks is crucial for TRE managers to safely use ML for data analysis.

Keywords:
artificial intelligenceconfidentialitydata enclavemachine learningoutput checkingtrusted research environment

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Area of Science:

  • Data Security
  • Computer Science
  • Statistical Disclosure Control

Background:

  • Trusted research environments (TREs) offer secure access to sensitive data, employing manual checks to mitigate disclosure risks.
  • Machine learning (ML) models, while powerful, introduce unique and scalable disclosure risks when trained on personal data within TREs.

Purpose of the Study:

  • To introduce TRE managers to the conceptual challenges posed by ML disclosure risks.
  • To outline ongoing work addressing these novel risks in TREs.

Main Methods:

  • Demonstrating the qualitatively different nature of ML disclosure risks compared to traditional statistical outputs.
  • Analyzing the scale and type of risks arising from ML model development.

Main Results:

  • Identifying a significant number of unresolved issues in ML disclosure risk management.
  • Highlighting progress in specific areas while acknowledging remaining uncertainties.
  • Presenting available remedial responses for TREs.

Conclusions:

  • Disclosure checking for ML models is currently a specialized field.
  • TRE managers require foundational knowledge of ML risks to make informed decisions about using TREs for ML development.