Computing Tight Differential Privacy Guarantees Using FFT

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dc.contributor Helsingin yliopisto, Tietojenkäsittelytieteen osasto fi
dc.contributor Helsingin yliopisto, Antti Honkela / Vastuullinen tutkija fi Koskela, Antti Honkela, Antti Jälkö, Joonas 2020-06-30T11:41:01Z 2020-06-30T11:41:01Z 2019-11-11
dc.identifier.citation Koskela , A , Honkela , A & Jälkö , J 2019 , ' Computing Tight Differential Privacy Guarantees Using FFT ' , TPDP 2019 - Theory and Practice of Differential Privacy CCS Workshop , Lontoo , Britannia , 11/11/2019 . en
dc.identifier.citation conference en
dc.identifier.other PURE: 139825457
dc.identifier.other PURE UUID: c1c4a958-b697-4005-8eda-03a4f3c2acd9
dc.identifier.other ORCID: /0000-0001-9193-8093/work/76725565
dc.description.abstract Computing privacy parameters for the differentially private stochastic gradient descent method (DP-SGD) is equivalent to analysing one dimensional mechanisms. We propose a numerical accountant for evaluating the (ε, δ)-privacy loss for mech- anisms with continuous one dimensional output. The proposed method is based on a numerical approximation of an integral formula which gives the tight (ε, δ)-values. The approximation is carried out by discretising the integral and by evaluating the resulting discrete convolutions using the fast Fourier transform algorithm. We focus on the subsampled Gaussian mechanism which underlies DP-SGD. We give both theoretical error bounds and numerical error estimates for the approximation. Experimen- tal comparisons with state-of-the-art techniques demonstrate significant improvements in bound tightness and/or computation time. Python code for the method can be found in Github ( fi
dc.language.iso fin
dc.rights en
dc.subject 113 Tietojenkäsittely- ja informaatiotieteet en
dc.title Computing Tight Differential Privacy Guarantees Using FFT fi
dc.type Posteri
dc.type.uri info:eu-repo/semantics/other

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