Security, privacy, and cryptography
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Opening Remarks: The Future of Privacy and Security
Security and privacy are key components to building trust in the technologies that we use, whether that be for applications for individuals, businesses, or in government. As new challenges and threats emerge in this area,…
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Research talk: Can causal learning improve the privacy of ML models?
Ensuring privacy of data used to train machine learning models is important for safe and responsible deployment of these models. At the same time, models are required to generalize across different data distributions to enable…
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Research talk: Building towards a responsible data economy
Speaker: Dawn Song, Professor, UC Berkeley Data is a key driver of the modern economy and AI/machine learning. However, a lot of this data is sensitive, and handling sensitive data has caused unprecedented challenges for…
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Research talk: DARPA SafeDocs: an approach to secure parsing and information interchange formats
Speaker: Sergey Bratus, Program Manager, DARPA DARPA and MITRE estimate that 80 percent of software security vulnerabilities have incorrect input validation as their root cause. In such scenarios, attackers provide malformed input, which, when not…
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Closing remarks: The Future of Privacy and Security
Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit
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Panel: Privacy preserving machine learning
In this panel, Microsoft’s Senior Researcher Shruti Tople will lead a discussion with prominent privacy and security researchers on the state of research in secure computation, differential privacy, and related technologies. The panel will explore…
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Technology demo: Using technology to combat human trafficking
Microsoft is a founding member of Tech Against Trafficking (TAT) – a coalition of organizations working to combat human trafficking with technology. In this session, we will introduce partnerships with the Counter Trafficking Data Collaborative…
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Keynote: Unlocking exabytes of training data through privacy preserving machine learning
Speaker: Jim Kleewein, Technical Fellow, Microsoft Data is the lifeblood of AI and machine learning. The better the model, and the better the data that model is trained on, the better the outcome. Today’s models…
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Panel: Building for societal resilience
We are learning that building for societal resilience requires change in how we pursue research and engineering. This panel discusses what we have learned during the COVID response: how existing projects adapted to new and…
