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Poster
On Computing Pairwise Statistics with Local Differential Privacy
Badih Ghazi · Pritish Kamath · Ravi Kumar · Pasin Manurangsi · Adam Sealfon

Tue Dec 12 03:15 PM -- 05:15 PM (PST) @ Great Hall & Hall B1+B2 #1601
in Poster Session 2 »
We study the problem of computing pairwise statistics, i.e., ones of the form $\binom{n}{2}^{-1} \sum_{i \ne j} f(x_i, x_j)$, where $x_i$ denotes the input to the $i$th user, with differential privacy (DP) in the local model. This formulation captures important metrics such as Kendall's $\tau$ coefficient, Area Under Curve, Gini's mean difference, Gini's entropy, etc. We give several novel and generic algorithms for the problem, leveraging techniques from DP algorithms for linear queries.

Author Information

Badih Ghazi (Google)
Pritish Kamath (Google Research)
Ravi Kumar (Google)
Pasin Manurangsi (Google Research)
Adam Sealfon (Google)

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