How can machine learning respect privacy without sacrificing fairness? Discover DPPL, a prototype-based method that provides strong privacy guarantees while boosting accuracy for underrepresented groups. By addressing bias in differentially private models, this approach ensures ethical and inclusive AI development without compromising performance.
As data breaches and privacy violations continue to make headlines, it is evident that mere reactive measures are not enough to protect personal data. Therefore, behind every privacy-aware organization lies an established software engineering process that systematically includes privacy engineering activities. Such activities include the selection of privacy-enhancing technologies, the analysis of potential privacy threats, as well as the continuous re-evaluation of privacy risks at runtime.
In this blog post, we give an overview of some of these activities which help your organization to build and operate privacy-friendly software by design. In doing so, we focus on risk-based privacy engineering as the driver for »Privacy by Design«.
On July 4, 2022, the Committee on Digital Affairs held a public hearing on “Digital identities” at the German Federal Parliament (Bundestag). Our expert Marian Margraf, Head of Secure Systems Engineering at Fraunhofer AISEC and Professor at Freie Universität Berlin, was invited to the event. He addressed in particular the use of the self-sovereign identity (SSI) principle in current solutions, for example in mobile end devices. In addition to the challenges presented by the widespread use of digital identities, he also outlined possible solutions for electronic trust services that are both secure and socially accepted. This blog article is an abridged transcript of his statement.