Learning to Limit Data Collection via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimization
Divya Shanmugam, Fernando Díaz, Samira Shabanian, Michèle Finck, Asia J. Biega
Abstract
Modern machine learning systems are increasingly characterized by extensive personal data collection, despite the diminishing returns and increasing societal costs of such practices. Yet, data minimisation is one of the core data protection principles enshrined in the European Union’s General Data Protection Regulation (’GDPR’) and requires that only personal data that is adequate, relevant and limited to what is necessary is processed. However, the principle has seen limited adoption due to the lack of technical interpretation.
Topics & Concepts
Minimisation (clinical trials)Computer scienceData Protection Act 1998Interpretation (philosophy)Scaling lawData collectionGeneral Data Protection RegulationMinificationEuropean unionData scienceRisk analysis (engineering)LawScalingComputer securityEconomicsPolitical scienceBusinessMathematicsStatisticsProgramming languageEconomic policyGeometryPrivacy-Preserving Technologies in DataEthics and Social Impacts of AIPrivacy, Security, and Data Protection