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        "author": "Antti Koskela",
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        "description": "# PLD-Accountant\n\nPython code for computing exact DP-guarantees for the subsampled Gaussian mechanism.  \n\nThe method is described in:\n\nAntti Koskela, Joonas J\u00e4lk\u00f6, Antti Honkela:  \nComputing Exact Guarantees for Differential Privacy  \nhttps://arxiv.org/abs/1906.03049  \n\n# Usage\n\nYou can download a PyPI package\n\n\n```\npip3 install pld-accountant\n```\n\nand run, for example,\n\n```\nfrom pld_accountant import compute_eps,compute_delta\n\nq=0.01\nsigma=1.2\nnc=1000 #number of compositions\n\ndelta=1e-5\n\na  = compute_eps.get_epsilon_bounded(q=q,sigma=sigma,target_delta=delta,ncomp=nc)\n\neps=2.0\n\nd  = compute_delta.get_delta_bounded(q=q,sigma=sigma,target_eps=eps,ncomp=nc)\n```\n\n\n",
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