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Privacy Engineering
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Input vs Output Privacy

Privacy in data systems has traditionally focused on protecting sensitive information as it enters a system - what we call input privacy. However, as systems become more complex and capable of inferring sensitive information from seemingly harmless data, the importance of output privacy has gained...

Privacy Engineering
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Birds of a Feather Leak Together: The Set Bias Privacy Problem

Secure multi-party computation (SMPC) enables organisations to collaborate on sensitive data analysis without directly sharing raw information. However, seemingly harmless aggregate outputs, particularly private set intersection (PSI), can leak individual-level information when analysed strategically over time. This post is based on research presented by Guo...