Security, privacy, and cryptography
Video
Tutorial, Research talk, and Q&A: ElectionGuard: Enabling voters to verify election integrity
00:00 Tutorial: ElectionGuard: Enabling voters to verify election integrity Speaker: Josh Benaloh, Senior Principal Cryptographer, Microsoft Research Redmond This tutorial will describe Microsoft’s free, open-source ElectionGuard toolkit, which can be incorporated into new and existing…
Video
Demo: EverParse: Automatic generation of formally verified secure parsers for cloud integrity
Speaker: Tahina Ramananandro, Principal Research Software Development Engineer, Microsoft Research Redmond DARPA and MITRE estimate that 80 percent of software security vulnerabilities have incorrect input validation as their root cause. In such scenarios, attackers provide…
Video
Research talks: Software supply chain security
The software development process has an extensive list of dependencies for building software, to include build inputs such as source code, open-source software (OSS), software development kits (SDKs), compilers, linkers, and other build tools. Securing…
Video
Panel: Data sharing in financial services: Unlocking new value with privacy-enhancing technologies
The financial services industry is on the verge of a foundational transformation, shifting from a set of discrete and self-contained functions to an intelligent set of services that embed within the experiences, business processes, and marketplaces…
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Research talk: Towards bridging between legal and technical approaches to data protection
Speaker: Kobbi Nissim, Professor, Georgetown University As computer systems become integrated into almost every aspect of society and are increasingly making decisions of legal significance, the need to bridge diverging (and sometimes conflicting) technical and…
Publication
Towards Generating Real-World Time Series Data
Microsoft Research Blog
Privacy Preserving Machine Learning: Maintaining confidentiality and preserving trust
Machine learning (ML) offers tremendous opportunities to increase productivity. However, ML systems are only as good as the quality of the data that informs the training of ML models. And training ML models requires a…
