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- W3016517038 abstract "This paper presents a differentially private algorithm for linear regression learning in a decentralized fashion. Under this algorithm, privacy budget is theoretically derived, in addition to that the solution error is shown to be bounded by $O(t)$ for $O(1/t)$ descent step size and $O(exp(t^{1-e}))$ for $O(t^{-e})$ descent step size." @default.
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- W3016517038 date "2020-04-16" @default.
- W3016517038 modified "2023-09-25" @default.
- W3016517038 title "Differentially Private Linear Regression over Fully Decentralized Datasets." @default.
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