facebookresearch / fbpcf
Private computation framework library allows developers to perform randomized controlled trials, without leaking information about who participated or what action an individual took. It uses secure multiparty computation to guarantee this privacy. It is suitable for conducting A/B testing, or measuring advertising lift and learning the aggregate statistics without sharing information on the individual level.
Summary
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| Main Code: 1,225 LOC (50 files) = H (60%) + CPP (35%) + CMAKE (4%) Secondary code: Test: 746 LOC (22); Generated: 0 LOC (0); Build & Deploy: 122 LOC (2); Other: 348 LOC (4); |
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| File Size: 0% long (>1000 LOC), 100% short (<= 200 LOC) | |||
| Unit Size: 0% long (>100 LOC), 68% short (<= 10 LOC) | |||
| Conditional Complexity: 0% complex (McCabe index > 50), 92% simple (McCabe index <= 5) | |||
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Logical Component Decomposition: primary (10 components) | ||
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1 year old
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0% of code updated more than 50 times Also see temporal dependencies for files frequently changed in same commits. |
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| Goals: Keep the system simple and easy to change (4) | |||
Latest commit date: 2022-01-11
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4
commits
(30 days)
4
contributors
(30 days) |
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