Nick Vincent

--- citation_key: staab2024verticaldataminimization entry_type: inproceedings title: 'From Principle to Practice: Vertical Data Minimization for Machine Learning' authors: - Robin Staab - Nikola Jovanović - Mislav Balunović - Martin Vechev year: '2024' venue: IEEE Symposium on Security and Privacy url: https://arxiv.org/abs/2311.10500 doi: 10.1109/SP54263.2024.00089 tags: - data minimization - machine learning - feature generalization - privacy --- Proposes vertical data minimization for ML through data generalization, reducing the need to collect full-resolution client data during training and deployment.

Nick Vincent

--- citation_key: pallas2022perquerydataminimization entry_type: inproceedings title: Configurable Per-Query Data Minimization for Privacy-Compliant Web APIs authors: - Frank Pallas - David Hartmann - Paul Heinrich - Josefine Kipke - Elias Grünewald year: '2022' venue: International Conference on Web Engineering url: https://arxiv.org/abs/2203.09903 doi: 10.1007/978-3-031-09917-5_22 tags: - data minimization - web APIs - GraphQL - privacy engineering --- Introduces Janus, a role-configurable GraphQL API layer for minimizing released personal data on a per-query basis.

Nick Vincent

--- citation_key: biega2020personalizationminimization entry_type: inproceedings title: Operationalizing the Legal Principle of Data Minimization for Personalization authors: - Asia J. Biega - Peter Potash - Hal Daumé III - Fernando Diaz - Michèle Finck year: '2020' venue: SIGIR url: https://arxiv.org/abs/2005.13718 doi: 10.1145/3397271.3401034 tags: - data minimization - personalization - recommender systems - GDPR --- Explores operational definitions of data minimization for personalization and recommender systems, including disparate impacts across users.

Nick Vincent

--- citation_key: pinisetty2018monitoringdataminimisation entry_type: misc title: Monitoring Data Minimisation authors: - Srinivas Pinisetty - Thibaud Antignac - David Sands - Gerardo Schneider year: '2018' venue: arXiv url: https://arxiv.org/abs/1801.02484 doi: 10.48550/arXiv.1801.02484 tags: - data minimization - runtime monitoring - formal methods --- Studies when data-minimization violations can be monitored at runtime for deterministic programs, including monolithic and distributed input settings.