9 citations · 12 across the 7 of their papers we have counts for
4 papers · 1 filter
Exact and Efficient Bayesian Inference for Privacy Risk Quantification (Extended Version)
Rasmus C. Rønneberg, Raúl Pardo, Andrzej Wąsowski
Data analysis has high value both for commercial and research purposes. However, disclosing analysis results may pose severe privacy risk to individuals. Privug is a method to quan…
Privacy with Good Taste: A Case Study in Quantifying Privacy Risks in Genetic Scores
Raúl Pardo, Willard Rafnsson, Gregor Steinhorn +5
Analysis of genetic data opens up many opportunities for medical and scientific advances. The use of phenotypic information and polygenic risk scores to analyze genetic data is wid…
Privug: Using Probabilistic Programming for Quantifying Leakage in Privacy Risk Analysis
Raúl Pardo, Willard Rafnsson, Christian Probst +1
Disclosure of data analytics results has important scientific and commercial justifications. However, no data shall be disclosed without a diligent investigation of risks for priva…
Analysis of Privacy Policies to Enhance Informed Consent (Extended Version)
Raúl Pardo, Daniel Le Métayer
In this report, we present an approach to enhance informed consent for the processing of personal data. The approach relies on a privacy policy language used to express, compare an…