)]}'
{
  "commit": "b4b26475acce0be9b7e9844db4af5a1b9929cab1",
  "tree": "9b59d1bcaf09f1f1127ef4d4e3d28d604ac47ed7",
  "parents": [
    "22648a237d150d648d76a68e8f30a4623e0b1a1d"
  ],
  "author": {
    "name": "Differential Privacy Team",
    "email": "noreply@google.com",
    "time": "Mon Dec 12 23:49:03 2022 -0800"
  },
  "committer": {
    "name": "Kulankhina",
    "email": "oleksandra.kulankhina@gmail.com",
    "time": "Thu Dec 15 17:53:11 2022 +0100"
  },
  "message": "Incorporating the \"Connect the Dots\" algorithm in PLD accounting library.\n\nReference: https://arxiv.org/abs/2207.04380\n\nMain changes are as follows:\n- Allow vectorized calls to get_delta_for_epsilon,\n- Construct pessimistic PLDPmf using Connect-the-Dots algorithm,\n- Support Connect-the-Dots algorithm for privacy loss distributions of additive noise mechanisms.\n\nSome other minor changes included as follows:\n- Add checks to make sure that truncation bounds in self convolution does not result in truncation of the input list,\n- Use more numerically stable scipy.special.logsumexp for moment generating function computation,\n- Use more efficient np.roll for shifting the output result for self convolution.\n\nPiperOrigin-RevId: 494208119\nChange-Id: I1c50896b3252c19c8802b1e1ebfd6f0ba94c3be2\nGitOrigin-RevId: fca6f9ba22ab0a7b3d020d3c5bc95d30405261df\n",
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