Imagine this: you need to share sensitive user data for a good cause (e.g. public-interest research, public health trends, fraud prevention) with someone you don't necessarily trust. How can you technically ensure data is protected while still allowing for precise insights?
Research scientists at TikTok developed ManaTEE, an open source confidential computing framework that solves exactly that problem. Using privacy-preserving and trusted execution technologies (PETs & TEE), this solution allows non-trusting parties to collaborate in the public interest. Originally developed last year to address TikTok's internal needs, the project has since been donated to the The Linux Foundation's Confidential Computing Consortium so that it benefits from an open governance model, accelerating it's development and adoption.
I am pumped to be supporting research scientists such as Dayeol Lee in the development of cutting-edge, privacy-centric, open-source technologies like ManaTEE. Take a look at a video of our participation at the (legendary) open source conference FOSDEM 2025 in Brussels intruducing the tool earlier this year:
https://lnkd.in/dqTi-9ZK
At #FOSDEM 2025, Dayeol Lee introduced #ManaTEE, an open source framework enabling secure, privacy-preserving data analytics. By leveraging Privacy-Enhancing Techniques (PETs) and Trusted Execution Environments (TEEs), ManaTEE empowers researchers to analyze sensitive data with confidence.
Now part of the Confidential Computing Consortium, ManaTEE is shaping the future of secure data collaboration. Learn more about the framework, its use cases, and how you can contribute: https://lnkd.in/emurv4zs
By Dayeol Lee, Research Scientist at TikTok Privacy Innovation Lab, and Mateus Guzzo, Open Source Advocate
#ConfidentialComputing #OpenSource #DataSecurity
