FBPCS (Facebook Private Computation Solutions) leverages secure multi-party computation (MPC) to output aggregated data without making unencrypted, readable data available to the other party or any third parties. Facebook provides impression & opportunity data, and the advertiser provides conversion / outcome data. Both parties have dedicated cloud computing instances living on separate Virtual Private Clouds (VPCs) that are connected to allow network communication. The FBPMP products that have been implemented are Private Lift and Private Attribution. It’s expected that more products will be implemented and added to the Private Measurement suite.
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| Main Code: 21,646 LOC (309 files) = PY (36%) + CPP (28%) + H (18%) + HPP (8%) + TF (5%) + JAVA (2%) + YML (<1%) + CMAKE (<1%) Secondary code: Test: 10,677 LOC (102); Generated: 0 LOC (0); Build & Deploy: 1,065 LOC (14); Other: 10,562 LOC (61); |
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| Duplication: 20% | |||
| File Size: 0% long (>1000 LOC), 74% short (<= 200 LOC) | |||
| Unit Size: 7% long (>100 LOC), 50% short (<= 10 LOC) | |||
| Conditional Complexity: 1% complex (McCabe index > 50), 68% simple (McCabe index <= 5) | |||
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Logical Component Decomposition: primary (17 components) | ||
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5 months 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) | |||
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Features of interest:
TODOs
41 files |
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Latest commit date: 2022-01-21
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24
commits
(30 days)
13
contributors
(30 days) |
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