safety security
Two Datasets Ask Whether They Should Meet
A two-party privacy protocol tests whether one classifier can fit both sensitive datasets without revealing either party's individual records.
Summary
A two-party privacy protocol tests whether one classifier can fit both sensitive datasets without revealing either party's individual records.
Two datasets may each be internally consistent while encoding incompatible labels or selection biases. Before pooling them, the parties want to know whether one hypothesis can fit both. This work formalizes a private compatibility test that distinguishes a perfect joint fit from at least a threshold number of unavoidable mistakes under pure two-party differential privacy. For thresholds on a finite real-line domain, the optimal disagreement threshold grows roughly with log log of the domain size divided by privacy; for halfspaces, it grows with dimension and log domain size, with protocols and lower bounds narrowing the range. The paper answers a theoretical pre-sharing question, not whether pooling is legally, ethically or operationally appropriate.
Why it matters
A two-party privacy protocol tests whether one classifier can fit both sensitive datasets without revealing either party's individual records.
Limits and context
- The paper answers a theoretical pre-sharing question, not whether pooling is legally, ethically or operationally appropriate.
Key claims
A two-party privacy protocol tests whether one classifier can fit both sensitive datasets without revealing either party's individual records.
Qualification: The paper answers a theoretical pre-sharing question, not whether pooling is legally, ethically or operationally appropriate.
Evidence: source-2026-11-03-015
Sources
- arXiv preprint 2611.03599, original v1arXiv · primary research
Corrections
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