What we've built and for whom — SwissAnon's funded research, Matthias Templ's earlier client engagements, and the partners we work with.
SwissAnon's own funded research.
What we didOur flagship project: privacy-preserving methods for event and longitudinal data — masking trajectories, protecting event histories, and pushing re-identification-risk assessment beyond k-anonymity.
ImpactOpen, reproducible tools that let organisations analyse and share data following the same people over time, with quantified risk.
What we didA risk-based anonymization and pseudonymization strategy under the EU GDPR/EDPB framework — starting from a formal disclosure-risk assessment and testing identity, attribute and inference disclosure, not k-anonymity alone.
ImpactA defensible route to sharing and using data: anonymization where the data allows, optimized pseudonymization where analytical utility requires.
SBB What we didHousehold-level anonymization and geographic protection of SBB's 9-million-person Synthetic Population (SynPop), with a formal disclosure-risk assessment for a differentiated data-release policy.
ImpactIndividual-level population data that can be shared and used safely in agent-based transport models — not the current all-or-nothing.
Anonymization and synthetic-data projects delivered before SwissAnon was founded:
What we didAnonymised census and survey microdata and delivered statistical-disclosure-control training; earlier, rebuilt the sdcMicro graphical interface and developed synthetic-data methods for safe microdata dissemination.
ImpactEnabled national statistical offices to share census and survey data with measured, controlled re-identification risk.
What we didDelivered statistical-disclosure-control tooling and synthetic-data generation methods under Eurostat special grant agreements.
ImpactStrengthened the open methods European statistical authorities rely on to release microdata safely.
What we didDeveloped synthetic-data and disclosure-control methods and trained statistical offices in developing countries, with the OECD/PARIS21 and the International Household Survey Network.
ImpactHelped resource-constrained statistical offices adopt reproducible, open-source anonymisation in routine data dissemination.
What we didAnonymised sensitive health-insurance data so it could be analysed and shared without exposing individual policyholders.
ImpactLet a major Swiss health insurer put its data to work within strict privacy constraints.
What we didAnonymised mobility and trajectory data — among the hardest data to protect, because movement patterns are highly identifying.
ImpactMade location-data analysis possible without leaving re-identifiable traces of individuals.
What we didAnonymised smart-meter energy-consumption data.
ImpactEnabled energy-usage analysis while protecting identifiable household-level patterns.
