Appendix_Variances.ipynb

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    "## References\n",
    "- [1] Erlingsson, Pihur, and Korolova (2014) \"RAPPOR: Randomized aggregatable privacy-preserving ordinal response\" (ACM CCS).\n",
    "- [2] Arcolezi et al (2022) \"Improving the Utility of Locally Differentially Private Protocols for Longitudinal and Multidimensional Frequency Estimates\" (Digital Communications and Networks).\n",
    "- [3] Wang et al (2017) \"Locally differentially private protocols for frequency estimation\" (USENIX Security)."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "import warnings; warnings.filterwarnings('ignore')\n",
    "import sympy\n",
    "from sympy import Symbol, exp, ln, Rational, sqrt\n",
    "\n",
    "from typing import Any\n",
    "exp:Any = exp\n",
    "    \n",
    "import numpy as np"
   ]
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     "text": [
      "RAPPOR\n"
     ]
    },
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       "$\\displaystyle - \\frac{\\left(- \\sqrt{\\left(4 e^{\\frac{7 \\epsilon_{\\infty}}{2}} - 4 e^{\\frac{5 \\epsilon_{\\infty}}{2}} - 4 e^{\\frac{3 \\epsilon_{\\infty}}{2}} + 4 e^{\\frac{\\epsilon_{\\infty}}{2}} + e^{4 \\epsilon_{\\infty}} + 4 e^{3 \\epsilon_{\\infty}} - 10 e^{2 \\epsilon_{\\infty}} + 4 e^{\\epsilon_{\\infty}} + 1\\right) e^{\\epsilon_{1}}} \\left(e^{\\epsilon_{1}} - 1\\right) \\left(e^{\\epsilon_{\\infty}} - 1\\right)^{2} - \\left(- \\sqrt{\\left(4 e^{\\frac{7 \\epsilon_{\\infty}}{2}} - 4 e^{\\frac{5 \\epsilon_{\\infty}}{2}} - 4 e^{\\frac{3 \\epsilon_{\\infty}}{2}} + 4 e^{\\frac{\\epsilon_{\\infty}}{2}} + e^{4 \\epsilon_{\\infty}} + 4 e^{3 \\epsilon_{\\infty}} - 10 e^{2 \\epsilon_{\\infty}} + 4 e^{\\epsilon_{\\infty}} + 1\\right) e^{\\epsilon_{1}}} \\left(e^{\\epsilon_{1}} - 1\\right) \\left(e^{\\epsilon_{\\infty}} - 1\\right)^{2} - \\left(e^{\\epsilon_{1}} - 1\\right) \\left(e^{\\epsilon_{\\infty}} - 1\\right)^{2} \\left(e^{\\epsilon_{1}} - e^{2 \\epsilon_{\\infty}} + 2 e^{\\epsilon_{\\infty}} - 2 e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right) + \\left(e^{\\epsilon_{1}} - e^{2 \\epsilon_{\\infty}} + 2 e^{\\epsilon_{\\infty}} - 2 e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right) \\left(e^{\\frac{3 \\epsilon_{\\infty}}{2}} - e^{\\frac{\\epsilon_{\\infty}}{2}} + e^{\\epsilon_{\\infty}} - e^{\\epsilon_{1} + \\frac{\\epsilon_{\\infty}}{2}} - e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + \\frac{3 \\epsilon_{\\infty}}{2}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right)\\right) e^{\\frac{\\epsilon_{\\infty}}{2}} + \\left(e^{\\epsilon_{1}} - e^{2 \\epsilon_{\\infty}} + 2 e^{\\epsilon_{\\infty}} - 2 e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right) \\left(e^{\\frac{3 \\epsilon_{\\infty}}{2}} - e^{\\frac{\\epsilon_{\\infty}}{2}} + e^{\\epsilon_{\\infty}} - e^{\\epsilon_{1} + \\frac{\\epsilon_{\\infty}}{2}} - e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + \\frac{3 \\epsilon_{\\infty}}{2}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right)\\right) \\left(- \\sqrt{\\left(4 e^{\\frac{7 \\epsilon_{\\infty}}{2}} - 4 e^{\\frac{5 \\epsilon_{\\infty}}{2}} - 4 e^{\\frac{3 \\epsilon_{\\infty}}{2}} + 4 e^{\\frac{\\epsilon_{\\infty}}{2}} + e^{4 \\epsilon_{\\infty}} + 4 e^{3 \\epsilon_{\\infty}} - 10 e^{2 \\epsilon_{\\infty}} + 4 e^{\\epsilon_{\\infty}} + 1\\right) e^{\\epsilon_{1}}} \\left(e^{\\epsilon_{1}} - 1\\right) \\left(e^{\\epsilon_{\\infty}} - 1\\right)^{2} - \\left(e^{\\epsilon_{1}} - 1\\right) \\left(e^{\\frac{\\epsilon_{\\infty}}{2}} + 1\\right) \\left(e^{\\epsilon_{\\infty}} - 1\\right)^{2} \\left(e^{\\epsilon_{1}} - e^{2 \\epsilon_{\\infty}} + 2 e^{\\epsilon_{\\infty}} - 2 e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right) - \\left(- \\sqrt{\\left(4 e^{\\frac{7 \\epsilon_{\\infty}}{2}} - 4 e^{\\frac{5 \\epsilon_{\\infty}}{2}} - 4 e^{\\frac{3 \\epsilon_{\\infty}}{2}} + 4 e^{\\frac{\\epsilon_{\\infty}}{2}} + e^{4 \\epsilon_{\\infty}} + 4 e^{3 \\epsilon_{\\infty}} - 10 e^{2 \\epsilon_{\\infty}} + 4 e^{\\epsilon_{\\infty}} + 1\\right) e^{\\epsilon_{1}}} \\left(e^{\\epsilon_{1}} - 1\\right) \\left(e^{\\epsilon_{\\infty}} - 1\\right)^{2} - \\left(e^{\\epsilon_{1}} - 1\\right) \\left(e^{\\epsilon_{\\infty}} - 1\\right)^{2} \\left(e^{\\epsilon_{1}} - e^{2 \\epsilon_{\\infty}} + 2 e^{\\epsilon_{\\infty}} - 2 e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right) + \\left(e^{\\epsilon_{1}} - e^{2 \\epsilon_{\\infty}} + 2 e^{\\epsilon_{\\infty}} - 2 e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right) \\left(e^{\\frac{3 \\epsilon_{\\infty}}{2}} - e^{\\frac{\\epsilon_{\\infty}}{2}} + e^{\\epsilon_{\\infty}} - e^{\\epsilon_{1} + \\frac{\\epsilon_{\\infty}}{2}} - e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + \\frac{3 \\epsilon_{\\infty}}{2}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right)\\right) e^{\\frac{\\epsilon_{\\infty}}{2}} + \\left(e^{\\epsilon_{1}} - e^{2 \\epsilon_{\\infty}} + 2 e^{\\epsilon_{\\infty}} - 2 e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right) \\left(e^{\\frac{3 \\epsilon_{\\infty}}{2}} - e^{\\frac{\\epsilon_{\\infty}}{2}} + e^{\\epsilon_{\\infty}} - e^{\\epsilon_{1} + \\frac{\\epsilon_{\\infty}}{2}} - e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + \\frac{3 \\epsilon_{\\infty}}{2}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right)\\right)}{n \\left(e^{\\frac{\\epsilon_{\\infty}}{2}} - 1\\right)^{2} \\left(- 2 \\sqrt{\\left(4 e^{\\frac{7 \\epsilon_{\\infty}}{2}} - 4 e^{\\frac{5 \\epsilon_{\\infty}}{2}} - 4 e^{\\frac{3 \\epsilon_{\\infty}}{2}} + 4 e^{\\frac{\\epsilon_{\\infty}}{2}} + e^{4 \\epsilon_{\\infty}} + 4 e^{3 \\epsilon_{\\infty}} - 10 e^{2 \\epsilon_{\\infty}} + 4 e^{\\epsilon_{\\infty}} + 1\\right) e^{\\epsilon_{1}}} \\left(e^{\\epsilon_{1}} - 1\\right) \\left(e^{\\epsilon_{\\infty}} - 1\\right)^{2} - \\left(e^{\\epsilon_{1}} - 1\\right) \\left(e^{\\epsilon_{\\infty}} - 1\\right)^{2} \\left(e^{\\epsilon_{1}} - e^{2 \\epsilon_{\\infty}} + 2 e^{\\epsilon_{\\infty}} - 2 e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right) + 2 \\left(e^{\\epsilon_{1}} - e^{2 \\epsilon_{\\infty}} + 2 e^{\\epsilon_{\\infty}} - 2 e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right) \\left(e^{\\frac{3 \\epsilon_{\\infty}}{2}} - e^{\\frac{\\epsilon_{\\infty}}{2}} + e^{\\epsilon_{\\infty}} - e^{\\epsilon_{1} + \\frac{\\epsilon_{\\infty}}{2}} - e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + \\frac{3 \\epsilon_{\\infty}}{2}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right)\\right)^{2}}$"
      ],
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       "-(-sqrt((4*exp(7*\\epsilon_\\infty/2) - 4*exp(5*\\epsilon_\\infty/2) - 4*exp(3*\\epsilon_\\infty/2) + 4*exp(\\epsilon_\\infty/2) + exp(4*\\epsilon_\\infty) + 4*exp(3*\\epsilon_\\infty) - 10*exp(2*\\epsilon_\\infty) + 4*exp(\\epsilon_\\infty) + 1)*exp(\\epsilon_1))*(exp(\\epsilon_1) - 1)*(exp(\\epsilon_\\infty) - 1)**2 - (-sqrt((4*exp(7*\\epsilon_\\infty/2) - 4*exp(5*\\epsilon_\\infty/2) - 4*exp(3*\\epsilon_\\infty/2) + 4*exp(\\epsilon_\\infty/2) + exp(4*\\epsilon_\\infty) + 4*exp(3*\\epsilon_\\infty) - 10*exp(2*\\epsilon_\\infty) + 4*exp(\\epsilon_\\infty) + 1)*exp(\\epsilon_1))*(exp(\\epsilon_1) - 1)*(exp(\\epsilon_\\infty) - 1)**2 - (exp(\\epsilon_1) - 1)*(exp(\\epsilon_\\infty) - 1)**2*(exp(\\epsilon_1) - exp(2*\\epsilon_\\infty) + 2*exp(\\epsilon_\\infty) - 2*exp(\\epsilon_1 + \\epsilon_\\infty) + exp(\\epsilon_1 + 2*\\epsilon_\\infty) - 1) + (exp(\\epsilon_1) - exp(2*\\epsilon_\\infty) + 2*exp(\\epsilon_\\infty) - 2*exp(\\epsilon_1 + \\epsilon_\\infty) + exp(\\epsilon_1 + 2*\\epsilon_\\infty) - 1)*(exp(3*\\epsilon_\\infty/2) - exp(\\epsilon_\\infty/2) + exp(\\epsilon_\\infty) - exp(\\epsilon_1 + \\epsilon_\\infty/2) - exp(\\epsilon_1 + \\epsilon_\\infty) + exp(\\epsilon_1 + 3*\\epsilon_\\infty/2) + exp(\\epsilon_1 + 2*\\epsilon_\\infty) - 1))*exp(\\epsilon_\\infty/2) + (exp(\\epsilon_1) - exp(2*\\epsilon_\\infty) + 2*exp(\\epsilon_\\infty) - 2*exp(\\epsilon_1 + \\epsilon_\\infty) + exp(\\epsilon_1 + 2*\\epsilon_\\infty) - 1)*(exp(3*\\epsilon_\\infty/2) - exp(\\epsilon_\\infty/2) + exp(\\epsilon_\\infty) - exp(\\epsilon_1 + \\epsilon_\\infty/2) - exp(\\epsilon_1 + \\epsilon_\\infty) + exp(\\epsilon_1 + 3*\\epsilon_\\infty/2) + exp(\\epsilon_1 + 2*\\epsilon_\\infty) - 1))*(-sqrt((4*exp(7*\\epsilon_\\infty/2) - 4*exp(5*\\epsilon_\\infty/2) - 4*exp(3*\\epsilon_\\infty/2) + 4*exp(\\epsilon_\\infty/2) + exp(4*\\epsilon_\\infty) + 4*exp(3*\\epsilon_\\infty) - 10*exp(2*\\epsilon_\\infty) + 4*exp(\\epsilon_\\infty) + 1)*exp(\\epsilon_1))*(exp(\\epsilon_1) - 1)*(exp(\\epsilon_\\infty) - 1)**2 - (exp(\\epsilon_1) - 1)*(exp(\\epsilon_\\infty/2) + 1)*(exp(\\epsilon_\\infty) - 1)**2*(exp(\\epsilon_1) - exp(2*\\epsilon_\\infty) + 2*exp(\\epsilon_\\infty) - 2*exp(\\epsilon_1 + \\epsilon_\\infty) + exp(\\epsilon_1 + 2*\\epsilon_\\infty) - 1) - (-sqrt((4*exp(7*\\epsilon_\\infty/2) - 4*exp(5*\\epsilon_\\infty/2) - 4*exp(3*\\epsilon_\\infty/2) + 4*exp(\\epsilon_\\infty/2) + exp(4*\\epsilon_\\infty) + 4*exp(3*\\epsilon_\\infty) - 10*exp(2*\\epsilon_\\infty) + 4*exp(\\epsilon_\\infty) + 1)*exp(\\epsilon_1))*(exp(\\epsilon_1) - 1)*(exp(\\epsilon_\\infty) - 1)**2 - (exp(\\epsilon_1) - 1)*(exp(\\epsilon_\\infty) - 1)**2*(exp(\\epsilon_1) - exp(2*\\epsilon_\\infty) + 2*exp(\\epsilon_\\infty) - 2*exp(\\epsilon_1 + \\epsilon_\\infty) + exp(\\epsilon_1 + 2*\\epsilon_\\infty) - 1) + (exp(\\epsilon_1) - exp(2*\\epsilon_\\infty) + 2*exp(\\epsilon_\\infty) - 2*exp(\\epsilon_1 + \\epsilon_\\infty) + exp(\\epsilon_1 + 2*\\epsilon_\\infty) - 1)*(exp(3*\\epsilon_\\infty/2) - exp(\\epsilon_\\infty/2) + exp(\\epsilon_\\infty) - exp(\\epsilon_1 + \\epsilon_\\infty/2) - exp(\\epsilon_1 + \\epsilon_\\infty) + exp(\\epsilon_1 + 3*\\epsilon_\\infty/2) + exp(\\epsilon_1 + 2*\\epsilon_\\infty) - 1))*exp(\\epsilon_\\infty/2) + (exp(\\epsilon_1) - exp(2*\\epsilon_\\infty) + 2*exp(\\epsilon_\\infty) - 2*exp(\\epsilon_1 + \\epsilon_\\infty) + exp(\\epsilon_1 + 2*\\epsilon_\\infty) - 1)*(exp(3*\\epsilon_\\infty/2) - exp(\\epsilon_\\infty/2) + exp(\\epsilon_\\infty) - exp(\\epsilon_1 + \\epsilon_\\infty/2) - exp(\\epsilon_1 + \\epsilon_\\infty) + exp(\\epsilon_1 + 3*\\epsilon_\\infty/2) + exp(\\epsilon_1 + 2*\\epsilon_\\infty) - 1))/(n*(exp(\\epsilon_\\infty/2) - 1)**2*(-2*sqrt((4*exp(7*\\epsilon_\\infty/2) - 4*exp(5*\\epsilon_\\infty/2) - 4*exp(3*\\epsilon_\\infty/2) + 4*exp(\\epsilon_\\infty/2) + exp(4*\\epsilon_\\infty) + 4*exp(3*\\epsilon_\\infty) - 10*exp(2*\\epsilon_\\infty) + 4*exp(\\epsilon_\\infty) + 1)*exp(\\epsilon_1))*(exp(\\epsilon_1) - 1)*(exp(\\epsilon_\\infty) - 1)**2 - (exp(\\epsilon_1) - 1)*(exp(\\epsilon_\\infty) - 1)**2*(exp(\\epsilon_1) - exp(2*\\epsilon_\\infty) + 2*exp(\\epsilon_\\infty) - 2*exp(\\epsilon_1 + \\epsilon_\\infty) + exp(\\epsilon_1 + 2*\\epsilon_\\infty) - 1) + 2*(exp(\\epsilon_1) - exp(2*\\epsilon_\\infty) + 2*exp(\\epsilon_\\infty) - 2*exp(\\epsilon_1 + \\epsilon_\\infty) + exp(\\epsilon_1 + 2*\\epsilon_\\infty) - 1)*(exp(3*\\epsilon_\\infty/2) - exp(\\epsilon_\\infty/2) + exp(\\epsilon_\\infty) - exp(\\epsilon_1 + \\epsilon_\\infty/2) - exp(\\epsilon_1 + \\epsilon_\\infty) + exp(\\epsilon_1 + 3*\\epsilon_\\infty/2) + exp(\\epsilon_1 + 2*\\epsilon_\\infty) - 1))**2)"
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     "text": [
      "$\\displaystyle - \\frac{\\left(- \\sqrt{\\left(4 e^{\\frac{7 \\epsilon_{\\infty}}{2}} - 4 e^{\\frac{5 \\epsilon_{\\infty}}{2}} - 4 e^{\\frac{3 \\epsilon_{\\infty}}{2}} + 4 e^{\\frac{\\epsilon_{\\infty}}{2}} + e^{4 \\epsilon_{\\infty}} + 4 e^{3 \\epsilon_{\\infty}} - 10 e^{2 \\epsilon_{\\infty}} + 4 e^{\\epsilon_{\\infty}} + 1\\right) e^{\\epsilon_{1}}} \\left(e^{\\epsilon_{1}} - 1\\right) \\left(e^{\\epsilon_{\\infty}} - 1\\right)^{2} - \\left(- \\sqrt{\\left(4 e^{\\frac{7 \\epsilon_{\\infty}}{2}} - 4 e^{\\frac{5 \\epsilon_{\\infty}}{2}} - 4 e^{\\frac{3 \\epsilon_{\\infty}}{2}} + 4 e^{\\frac{\\epsilon_{\\infty}}{2}} + e^{4 \\epsilon_{\\infty}} + 4 e^{3 \\epsilon_{\\infty}} - 10 e^{2 \\epsilon_{\\infty}} + 4 e^{\\epsilon_{\\infty}} + 1\\right) e^{\\epsilon_{1}}} \\left(e^{\\epsilon_{1}} - 1\\right) \\left(e^{\\epsilon_{\\infty}} - 1\\right)^{2} - \\left(e^{\\epsilon_{1}} - 1\\right) \\left(e^{\\epsilon_{\\infty}} - 1\\right)^{2} \\left(e^{\\epsilon_{1}} - e^{2 \\epsilon_{\\infty}} + 2 e^{\\epsilon_{\\infty}} - 2 e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right) + \\left(e^{\\epsilon_{1}} - e^{2 \\epsilon_{\\infty}} + 2 e^{\\epsilon_{\\infty}} - 2 e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right) \\left(e^{\\frac{3 \\epsilon_{\\infty}}{2}} - e^{\\frac{\\epsilon_{\\infty}}{2}} + e^{\\epsilon_{\\infty}} - e^{\\epsilon_{1} + \\frac{\\epsilon_{\\infty}}{2}} - e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + \\frac{3 \\epsilon_{\\infty}}{2}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right)\\right) e^{\\frac{\\epsilon_{\\infty}}{2}} + \\left(e^{\\epsilon_{1}} - e^{2 \\epsilon_{\\infty}} + 2 e^{\\epsilon_{\\infty}} - 2 e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right) \\left(e^{\\frac{3 \\epsilon_{\\infty}}{2}} - e^{\\frac{\\epsilon_{\\infty}}{2}} + e^{\\epsilon_{\\infty}} - e^{\\epsilon_{1} + \\frac{\\epsilon_{\\infty}}{2}} - e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + \\frac{3 \\epsilon_{\\infty}}{2}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right)\\right) \\left(- \\sqrt{\\left(4 e^{\\frac{7 \\epsilon_{\\infty}}{2}} - 4 e^{\\frac{5 \\epsilon_{\\infty}}{2}} - 4 e^{\\frac{3 \\epsilon_{\\infty}}{2}} + 4 e^{\\frac{\\epsilon_{\\infty}}{2}} + e^{4 \\epsilon_{\\infty}} + 4 e^{3 \\epsilon_{\\infty}} - 10 e^{2 \\epsilon_{\\infty}} + 4 e^{\\epsilon_{\\infty}} + 1\\right) e^{\\epsilon_{1}}} \\left(e^{\\epsilon_{1}} - 1\\right) \\left(e^{\\epsilon_{\\infty}} - 1\\right)^{2} - \\left(e^{\\epsilon_{1}} - 1\\right) \\left(e^{\\frac{\\epsilon_{\\infty}}{2}} + 1\\right) \\left(e^{\\epsilon_{\\infty}} - 1\\right)^{2} \\left(e^{\\epsilon_{1}} - e^{2 \\epsilon_{\\infty}} + 2 e^{\\epsilon_{\\infty}} - 2 e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right) - \\left(- \\sqrt{\\left(4 e^{\\frac{7 \\epsilon_{\\infty}}{2}} - 4 e^{\\frac{5 \\epsilon_{\\infty}}{2}} - 4 e^{\\frac{3 \\epsilon_{\\infty}}{2}} + 4 e^{\\frac{\\epsilon_{\\infty}}{2}} + e^{4 \\epsilon_{\\infty}} + 4 e^{3 \\epsilon_{\\infty}} - 10 e^{2 \\epsilon_{\\infty}} + 4 e^{\\epsilon_{\\infty}} + 1\\right) e^{\\epsilon_{1}}} \\left(e^{\\epsilon_{1}} - 1\\right) \\left(e^{\\epsilon_{\\infty}} - 1\\right)^{2} - \\left(e^{\\epsilon_{1}} - 1\\right) \\left(e^{\\epsilon_{\\infty}} - 1\\right)^{2} \\left(e^{\\epsilon_{1}} - e^{2 \\epsilon_{\\infty}} + 2 e^{\\epsilon_{\\infty}} - 2 e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right) + \\left(e^{\\epsilon_{1}} - e^{2 \\epsilon_{\\infty}} + 2 e^{\\epsilon_{\\infty}} - 2 e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right) \\left(e^{\\frac{3 \\epsilon_{\\infty}}{2}} - e^{\\frac{\\epsilon_{\\infty}}{2}} + e^{\\epsilon_{\\infty}} - e^{\\epsilon_{1} + \\frac{\\epsilon_{\\infty}}{2}} - e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + \\frac{3 \\epsilon_{\\infty}}{2}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right)\\right) e^{\\frac{\\epsilon_{\\infty}}{2}} + \\left(e^{\\epsilon_{1}} - e^{2 \\epsilon_{\\infty}} + 2 e^{\\epsilon_{\\infty}} - 2 e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right) \\left(e^{\\frac{3 \\epsilon_{\\infty}}{2}} - e^{\\frac{\\epsilon_{\\infty}}{2}} + e^{\\epsilon_{\\infty}} - e^{\\epsilon_{1} + \\frac{\\epsilon_{\\infty}}{2}} - e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + \\frac{3 \\epsilon_{\\infty}}{2}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right)\\right)}{n \\left(e^{\\frac{\\epsilon_{\\infty}}{2}} - 1\\right)^{2} \\left(- 2 \\sqrt{\\left(4 e^{\\frac{7 \\epsilon_{\\infty}}{2}} - 4 e^{\\frac{5 \\epsilon_{\\infty}}{2}} - 4 e^{\\frac{3 \\epsilon_{\\infty}}{2}} + 4 e^{\\frac{\\epsilon_{\\infty}}{2}} + e^{4 \\epsilon_{\\infty}} + 4 e^{3 \\epsilon_{\\infty}} - 10 e^{2 \\epsilon_{\\infty}} + 4 e^{\\epsilon_{\\infty}} + 1\\right) e^{\\epsilon_{1}}} \\left(e^{\\epsilon_{1}} - 1\\right) \\left(e^{\\epsilon_{\\infty}} - 1\\right)^{2} - \\left(e^{\\epsilon_{1}} - 1\\right) \\left(e^{\\epsilon_{\\infty}} - 1\\right)^{2} \\left(e^{\\epsilon_{1}} - e^{2 \\epsilon_{\\infty}} + 2 e^{\\epsilon_{\\infty}} - 2 e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right) + 2 \\left(e^{\\epsilon_{1}} - e^{2 \\epsilon_{\\infty}} + 2 e^{\\epsilon_{\\infty}} - 2 e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right) \\left(e^{\\frac{3 \\epsilon_{\\infty}}{2}} - e^{\\frac{\\epsilon_{\\infty}}{2}} + e^{\\epsilon_{\\infty}} - e^{\\epsilon_{1} + \\frac{\\epsilon_{\\infty}}{2}} - e^{\\epsilon_{1} + \\epsilon_{\\infty}} + e^{\\epsilon_{1} + \\frac{3 \\epsilon_{\\infty}}{2}} + e^{\\epsilon_{1} + 2 \\epsilon_{\\infty}} - 1\\right)\\right)^{2}}$\n",
      "--------------------\n",
      "L_OSUE\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{4 e^{\\epsilon_{1}}}{n \\left(e^{2 \\epsilon_{1}} - 2 e^{\\epsilon_{1}} + 1\\right)}$"
      ],
      "text/plain": [
       "4*exp(\\epsilon_1)/(n*(exp(2*\\epsilon_1) - 2*exp(\\epsilon_1) + 1))"
      ]
     },
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    },
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     "output_type": "stream",
     "text": [
      "$\\displaystyle \\frac{4 e^{\\epsilon_{1}}}{n \\left(e^{2 \\epsilon_{1}} - 2 e^{\\epsilon_{1}} + 1\\right)}$\n",
      "--------------------\n",
      "L_GRR\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle - \\frac{\\left(\\left(1 - e^{\\epsilon_{1} + \\epsilon_{\\infty}}\\right) \\left(- \\left(k - 1\\right) \\left(e^{\\epsilon_{1}} - e^{\\epsilon_{\\infty}}\\right) + e^{\\epsilon_{1} + \\epsilon_{\\infty}} - 1\\right) + \\left(e^{\\epsilon_{1}} - e^{\\epsilon_{\\infty}}\\right) \\left(- k - e^{\\epsilon_{\\infty}} + 2\\right) \\left(\\left(k - 1\\right) \\left(e^{\\epsilon_{1}} - e^{\\epsilon_{\\infty}}\\right) - e^{\\epsilon_{1} + \\epsilon_{\\infty}} + 1\\right)\\right) \\left(- \\left(1 - e^{\\epsilon_{1} + \\epsilon_{\\infty}}\\right) \\left(\\left(k - 1\\right) \\left(- e^{\\epsilon_{1}} + e^{\\epsilon_{\\infty}}\\right) + e^{\\epsilon_{1} + \\epsilon_{\\infty}} - 1\\right) - \\left(- e^{\\epsilon_{1}} + e^{\\epsilon_{\\infty}}\\right) \\left(- k - e^{\\epsilon_{\\infty}} + 2\\right) \\left(- \\left(k - 1\\right) \\left(e^{\\epsilon_{1}} - e^{\\epsilon_{\\infty}}\\right) + e^{\\epsilon_{1} + \\epsilon_{\\infty}} - 1\\right) - \\left(k + e^{\\epsilon_{\\infty}} - 1\\right) \\left(\\left(k - 1\\right) \\left(- e^{\\epsilon_{1}} + e^{\\epsilon_{\\infty}}\\right) + e^{\\epsilon_{1} + \\epsilon_{\\infty}} - 1\\right) \\left(- \\left(k - 1\\right) \\left(e^{\\epsilon_{1}} - e^{\\epsilon_{\\infty}}\\right) + e^{\\epsilon_{1} + \\epsilon_{\\infty}} - 1\\right)\\right) \\left(\\left(k - 1\\right) \\left(e^{\\epsilon_{1}} - e^{\\epsilon_{\\infty}}\\right) - e^{\\epsilon_{1} + \\epsilon_{\\infty}} + 1\\right)}{n \\left(1 - e^{\\epsilon_{\\infty}}\\right)^{2} \\left(\\left(1 - e^{\\epsilon_{1} + \\epsilon_{\\infty}}\\right) \\left(- \\left(k - 1\\right) \\left(e^{\\epsilon_{1}} - e^{\\epsilon_{\\infty}}\\right) + e^{\\epsilon_{1} + \\epsilon_{\\infty}} - 1\\right) + \\left(e^{\\epsilon_{1}} - e^{\\epsilon_{\\infty}}\\right) \\left(\\left(k - 1\\right) \\left(e^{\\epsilon_{1}} - e^{\\epsilon_{\\infty}}\\right) - e^{\\epsilon_{1} + \\epsilon_{\\infty}} + 1\\right)\\right)^{2} \\left(\\left(k - 1\\right) \\left(- e^{\\epsilon_{1}} + e^{\\epsilon_{\\infty}}\\right) + e^{\\epsilon_{1} + \\epsilon_{\\infty}} - 1\\right)}$"
      ],
      "text/plain": [
       "-((1 - exp(\\epsilon_1 + \\epsilon_\\infty))*(-(k - 1)*(exp(\\epsilon_1) - exp(\\epsilon_\\infty)) + exp(\\epsilon_1 + \\epsilon_\\infty) - 1) + (exp(\\epsilon_1) - exp(\\epsilon_\\infty))*(-k - exp(\\epsilon_\\infty) + 2)*((k - 1)*(exp(\\epsilon_1) - exp(\\epsilon_\\infty)) - exp(\\epsilon_1 + \\epsilon_\\infty) + 1))*(-(1 - exp(\\epsilon_1 + \\epsilon_\\infty))*((k - 1)*(-exp(\\epsilon_1) + exp(\\epsilon_\\infty)) + exp(\\epsilon_1 + \\epsilon_\\infty) - 1) - (-exp(\\epsilon_1) + exp(\\epsilon_\\infty))*(-k - exp(\\epsilon_\\infty) + 2)*(-(k - 1)*(exp(\\epsilon_1) - exp(\\epsilon_\\infty)) + exp(\\epsilon_1 + \\epsilon_\\infty) - 1) - (k + exp(\\epsilon_\\infty) - 1)*((k - 1)*(-exp(\\epsilon_1) + exp(\\epsilon_\\infty)) + exp(\\epsilon_1 + \\epsilon_\\infty) - 1)*(-(k - 1)*(exp(\\epsilon_1) - exp(\\epsilon_\\infty)) + exp(\\epsilon_1 + \\epsilon_\\infty) - 1))*((k - 1)*(exp(\\epsilon_1) - exp(\\epsilon_\\infty)) - exp(\\epsilon_1 + \\epsilon_\\infty) + 1)/(n*(1 - exp(\\epsilon_\\infty))**2*((1 - exp(\\epsilon_1 + \\epsilon_\\infty))*(-(k - 1)*(exp(\\epsilon_1) - exp(\\epsilon_\\infty)) + exp(\\epsilon_1 + \\epsilon_\\infty) - 1) + (exp(\\epsilon_1) - exp(\\epsilon_\\infty))*((k - 1)*(exp(\\epsilon_1) - exp(\\epsilon_\\infty)) - exp(\\epsilon_1 + \\epsilon_\\infty) + 1))**2*((k - 1)*(-exp(\\epsilon_1) + exp(\\epsilon_\\infty)) + exp(\\epsilon_1 + \\epsilon_\\infty) - 1))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$\\displaystyle - \\frac{\\left(\\left(1 - e^{\\epsilon_{1} + \\epsilon_{\\infty}}\\right) \\left(- \\left(k - 1\\right) \\left(e^{\\epsilon_{1}} - e^{\\epsilon_{\\infty}}\\right) + e^{\\epsilon_{1} + \\epsilon_{\\infty}} - 1\\right) + \\left(e^{\\epsilon_{1}} - e^{\\epsilon_{\\infty}}\\right) \\left(- k - e^{\\epsilon_{\\infty}} + 2\\right) \\left(\\left(k - 1\\right) \\left(e^{\\epsilon_{1}} - e^{\\epsilon_{\\infty}}\\right) - e^{\\epsilon_{1} + \\epsilon_{\\infty}} + 1\\right)\\right) \\left(- \\left(1 - e^{\\epsilon_{1} + \\epsilon_{\\infty}}\\right) \\left(\\left(k - 1\\right) \\left(- e^{\\epsilon_{1}} + e^{\\epsilon_{\\infty}}\\right) + e^{\\epsilon_{1} + \\epsilon_{\\infty}} - 1\\right) - \\left(- e^{\\epsilon_{1}} + e^{\\epsilon_{\\infty}}\\right) \\left(- k - e^{\\epsilon_{\\infty}} + 2\\right) \\left(- \\left(k - 1\\right) \\left(e^{\\epsilon_{1}} - e^{\\epsilon_{\\infty}}\\right) + e^{\\epsilon_{1} + \\epsilon_{\\infty}} - 1\\right) - \\left(k + e^{\\epsilon_{\\infty}} - 1\\right) \\left(\\left(k - 1\\right) \\left(- e^{\\epsilon_{1}} + e^{\\epsilon_{\\infty}}\\right) + e^{\\epsilon_{1} + \\epsilon_{\\infty}} - 1\\right) \\left(- \\left(k - 1\\right) \\left(e^{\\epsilon_{1}} - e^{\\epsilon_{\\infty}}\\right) + e^{\\epsilon_{1} + \\epsilon_{\\infty}} - 1\\right)\\right) \\left(\\left(k - 1\\right) \\left(e^{\\epsilon_{1}} - e^{\\epsilon_{\\infty}}\\right) - e^{\\epsilon_{1} + \\epsilon_{\\infty}} + 1\\right)}{n \\left(1 - e^{\\epsilon_{\\infty}}\\right)^{2} \\left(\\left(1 - e^{\\epsilon_{1} + \\epsilon_{\\infty}}\\right) \\left(- \\left(k - 1\\right) \\left(e^{\\epsilon_{1}} - e^{\\epsilon_{\\infty}}\\right) + e^{\\epsilon_{1} + \\epsilon_{\\infty}} - 1\\right) + \\left(e^{\\epsilon_{1}} - e^{\\epsilon_{\\infty}}\\right) \\left(\\left(k - 1\\right) \\left(e^{\\epsilon_{1}} - e^{\\epsilon_{\\infty}}\\right) - e^{\\epsilon_{1} + \\epsilon_{\\infty}} + 1\\right)\\right)^{2} \\left(\\left(k - 1\\right) \\left(- e^{\\epsilon_{1}} + e^{\\epsilon_{\\infty}}\\right) + e^{\\epsilon_{1} + \\epsilon_{\\infty}} - 1\\right)}$\n",
      "--------------------\n",
      "dBitFlip\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{b}{2 d n \\sinh{\\left(\\frac{\\epsilon_{\\infty}}{2} \\right)}}$"
      ],
      "text/plain": [
       "b/(2*d*n*sinh(\\epsilon_\\infty/2))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$\\displaystyle \\frac{b}{2 d n \\sinh{\\left(\\frac{\\epsilon_{\\infty}}{2} \\right)}}$\n",
      "--------------------\n",
      "LOLOHA\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle - \\frac{\\left(\\left(e^{\\epsilon_{1}} - e^{\\epsilon_{\\infty}}\\right) \\left(g + e^{\\epsilon_{\\infty}} - 2\\right) - e^{\\epsilon_{1} + \\epsilon_{\\infty}} + 1\\right) \\left(\\left(e^{\\epsilon_{1}} - e^{\\epsilon_{\\infty}}\\right) \\left(g + e^{\\epsilon_{\\infty}} - 2\\right) - \\left(g + e^{\\epsilon_{\\infty}} - 1\\right) \\left(\\left(g - 1\\right) \\left(e^{\\epsilon_{1}} - e^{\\epsilon_{\\infty}}\\right) - e^{\\epsilon_{1} + \\epsilon_{\\infty}} + 1\\right) - e^{\\epsilon_{1} + \\epsilon_{\\infty}} + 1\\right)}{n \\left(e^{\\epsilon_{\\infty}} - 1\\right)^{2} \\left(e^{\\epsilon_{1}} - e^{\\epsilon_{\\infty}} + e^{\\epsilon_{1} + \\epsilon_{\\infty}} - 1\\right)^{2}}$"
      ],
      "text/plain": [
       "-((exp(\\epsilon_1) - exp(\\epsilon_\\infty))*(g + exp(\\epsilon_\\infty) - 2) - exp(\\epsilon_1 + \\epsilon_\\infty) + 1)*((exp(\\epsilon_1) - exp(\\epsilon_\\infty))*(g + exp(\\epsilon_\\infty) - 2) - (g + exp(\\epsilon_\\infty) - 1)*((g - 1)*(exp(\\epsilon_1) - exp(\\epsilon_\\infty)) - exp(\\epsilon_1 + \\epsilon_\\infty) + 1) - exp(\\epsilon_1 + \\epsilon_\\infty) + 1)/(n*(exp(\\epsilon_\\infty) - 1)**2*(exp(\\epsilon_1) - exp(\\epsilon_\\infty) + exp(\\epsilon_1 + \\epsilon_\\infty) - 1)**2)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$\\displaystyle - \\frac{\\left(\\left(e^{\\epsilon_{1}} - e^{\\epsilon_{\\infty}}\\right) \\left(g + e^{\\epsilon_{\\infty}} - 2\\right) - e^{\\epsilon_{1} + \\epsilon_{\\infty}} + 1\\right) \\left(\\left(e^{\\epsilon_{1}} - e^{\\epsilon_{\\infty}}\\right) \\left(g + e^{\\epsilon_{\\infty}} - 2\\right) - \\left(g + e^{\\epsilon_{\\infty}} - 1\\right) \\left(\\left(g - 1\\right) \\left(e^{\\epsilon_{1}} - e^{\\epsilon_{\\infty}}\\right) - e^{\\epsilon_{1} + \\epsilon_{\\infty}} + 1\\right) - e^{\\epsilon_{1} + \\epsilon_{\\infty}} + 1\\right)}{n \\left(e^{\\epsilon_{\\infty}} - 1\\right)^{2} \\left(e^{\\epsilon_{1}} - e^{\\epsilon_{\\infty}} + e^{\\epsilon_{1} + \\epsilon_{\\infty}} - 1\\right)^{2}}$\n",
      "--------------------\n"
     ]
    }
   ],
   "source": [
    "one = Rational(1,1)\n",
    "k = Symbol('k')\n",
    "n = Symbol('n')\n",
    "d = Symbol('d')\n",
    "b = Symbol('b')\n",
    "g = Symbol('g')\n",
    "epsInf = Symbol('\\\\epsilon_\\\\infty')\n",
    "epsOne = Symbol('\\\\epsilon_1')\n",
    "epsIrr = ln((exp(epsInf+epsOne)-1) / (exp(epsInf)-exp(epsOne)))\n",
    "\n",
    "def varStar(p1,q1,p2,q2):\n",
    "    return (p2*q1-q2*(q1-1))*(-p2*q1+q2*(q1-1)+1) / n / (p1-q1)**2 / (p2-q2)**2\n",
    "\n",
    "def varStarSingleRound(p,q):\n",
    "    return (q*(1-q))/n/(p-q)\n",
    "\n",
    "def RAPPOR():\n",
    "    p1 = exp(epsInf/2) / (1 + exp(epsInf/2))\n",
    "    q1 = 1 / (1 + exp(epsInf/2))\n",
    "    p2 = - (sqrt((4 * exp(7 * epsInf / 2) - 4 * exp(5 * epsInf / 2) - 4 * exp(3 * epsInf / 2) + 4 * exp(epsInf / 2) + exp(4 * epsInf) + 4 * exp(3 * epsInf) - 10 * exp(2 * epsInf) + 4 * exp(epsInf) + 1) * exp(epsOne)) * (exp(epsOne) - 1) * (exp(epsInf) - 1) ** 2 - (exp(epsOne) - exp(2 * epsInf) + 2 * exp(epsInf) - 2 * exp( epsOne + epsInf) + exp(epsOne + 2 * epsInf) - 1) * (exp(3 * epsInf / 2) - exp(epsInf / 2) + exp(epsInf) - exp( epsOne + epsInf / 2) - exp(epsOne + epsInf) + exp(epsOne + 3 * epsInf / 2) + exp( epsOne + 2 * epsInf) - 1)) / ((exp(epsOne) - 1) * (exp(epsInf) - 1) ** 2 * (exp(epsOne) - exp(2 * epsInf) + 2 * exp(epsInf) - 2 * exp(epsOne + epsInf) + exp(epsOne + 2 * epsInf) - 1))\n",
    "    q2 = 1 - p2\n",
    "    vStar = varStar(p1, q1, p2, q2)\n",
    "    return vStar\n",
    "\n",
    "def L_OSUE():\n",
    "    p1 = one / 2\n",
    "    q1 = 1 / (1 + exp(epsInf))\n",
    "    p2 = (exp(epsInf)*exp(epsOne) - 1)\n",
    "    p2 /= exp(epsInf) - exp(epsOne) + exp(epsInf+epsOne) - 1\n",
    "    q2 = 1 - p2\n",
    "    vStar = varStar(p1, q1, p2, q2)\n",
    "    return vStar\n",
    "\n",
    "def L_GRR():\n",
    "    p1 = exp(epsInf) / (exp(epsInf)+k-1)\n",
    "    q1 = (1 - p1) / (k-1)\n",
    "    p2 = exp(epsInf+epsOne) - 1\n",
    "    p2 /= (k-1) * (exp(epsInf) - exp(epsOne)) + exp(epsInf+epsOne) - 1\n",
    "    q2 = (1 - p2) / (k-1)\n",
    "    vStar = varStar(p1, q1, p2, q2)\n",
    "    return vStar\n",
    "\n",
    "def dBitFlip():\n",
    "    # p, q as in Rappor\n",
    "    p = exp(epsInf/2) / (1 + exp(epsInf/2))\n",
    "    q = 1 / (1 + exp(epsInf/2))\n",
    "    vStar = b/d * varStarSingleRound(p, q)\n",
    "    return vStar\n",
    "\n",
    "def LOLOHA():\n",
    "    p1 = exp(epsInf) / (g-1 + exp(epsInf))\n",
    "    q1 = 1 / (g-1 + exp(epsInf))\n",
    "    p2 = exp(epsIrr) / (g-1 + exp(epsIrr))\n",
    "    q2 = 1 / (g-1 + exp(epsIrr))\n",
    "    vStar = varStar(p1, q1, p2, q2)\n",
    "    return vStar\n",
    "\n",
    "\n",
    "n = Symbol('n')\n",
    "\n",
    "functions = [RAPPOR, L_OSUE, L_GRR, dBitFlip, LOLOHA]\n",
    "\n",
    "for f in functions:\n",
    "    print(f.__name__)\n",
    "    vStar = sympy.simplify(f())\n",
    "    display(vStar)\n",
    "    print(vStar._repr_latex_())\n",
    "    print('-'*20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical analysis of approximate variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Approximate_var_f_l(p1, q1, p2, q2, n = 10000):       \n",
    "    \n",
    "    f = 0 # approximate variance\n",
    "    \n",
    "    sig = q1*p2 + (1 - q1) * q2 + f*(((p1*p2) - p1*(1-p2)) + (q2*(1-p1) - (1-q2)*(1-p1)))\n",
    "    \n",
    "    return (sig * (1 - sig)) / (n * (p1 - q1)**2 * (p2 - q2)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# upper bound, i.e., eps_inf\n",
    "lst_eps = np.arange(0.5, 5.1, 0.5)\n",
    "\n",
    "# alpha values\n",
    "lst_alpha = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving variance values\n",
    "var_l_sue = {alpha:[] for alpha in lst_alpha} # RAPPOR [1]\n",
    "var_l_osue = {alpha:[] for alpha in lst_alpha} # L-OSUE [2]\n",
    "var_ololoha = {alpha:[] for alpha in lst_alpha} \n",
    "var_biloloha = {alpha:[] for alpha in lst_alpha}\n",
    "lst_k = [96] # e.g., \"hours-per-week\" attribute used with Adult dataset\n",
    "var_l_grr = {k:{alpha:[] for alpha in lst_alpha} for k in lst_k} # L-GRR [2]\n",
    "\n",
    "for alpha in lst_alpha:\n",
    "\n",
    "    for eps_perm in lst_eps:\n",
    "        eps_1 = alpha * eps_perm\n",
    "        \n",
    "        for k in lst_k:\n",
    "            # L-GRR parameters\n",
    "            p1_grr = np.exp(eps_perm) / (np.exp(eps_perm) + k - 1)\n",
    "            q1_grr = (1 - p1_grr) / (k - 1)\n",
    "\n",
    "            p2_grr = (q1_grr - np.exp(eps_1) * p1_grr) / ((-p1_grr * np.exp(eps_1)) + k*q1_grr*np.exp(eps_1) - q1_grr*np.exp(eps_1) - p1_grr*(k-1)+q1_grr)\n",
    "            q2_grr = (1 - p2_grr) / (k-1)\n",
    "            \n",
    "            var_l_grr[k][alpha].append(Approximate_var_f_l(p1_grr, q1_grr, p2_grr, q2_grr))\n",
    "\n",
    "        # RAPPOR parameters\n",
    "        p1_sue = np.exp(eps_perm/2) / (np.exp(eps_perm/2) + 1)\n",
    "        q1_sue = 1 / (np.exp(eps_perm/2) + 1)\n",
    "\n",
    "        p2_sue = - (np.sqrt((4 * np.exp(7 * eps_perm / 2) - 4 * np.exp(5 * eps_perm / 2) - 4 * np.exp(3 * eps_perm / 2) + 4 * np.exp(eps_perm / 2) + np.exp(4 * eps_perm) + 4 * np.exp(3 * eps_perm) - 10 * np.exp(2 * eps_perm) + 4 * np.exp(eps_perm) + 1) * np.exp(eps_1)) * (np.exp(eps_1) - 1) * (np.exp(eps_perm) - 1) ** 2 - (np.exp(eps_1) - np.exp(2 * eps_perm) + 2 * np.exp(eps_perm) - 2 * np.exp(eps_1 + eps_perm) + np.exp(eps_1 + 2 * eps_perm) - 1) * (np.exp(3 * eps_perm / 2) - np.exp(eps_perm / 2) + np.exp(eps_perm) - np.exp(eps_1 + eps_perm / 2) - np.exp(eps_1 + eps_perm) + np.exp(eps_1 + 3 * eps_perm / 2) + np.exp(eps_1 + 2 * eps_perm) - 1)) / ((np.exp(eps_1) - 1) * (np.exp(eps_perm) - 1) ** 2 * (np.exp(eps_1) - np.exp(2 * eps_perm) + 2 * np.exp(eps_perm) - 2 * np.exp(eps_1 + eps_perm) + np.exp(eps_1 + 2 * eps_perm) - 1))\n",
    "        q2_sue = 1 - p2_sue\n",
    "\n",
    "        var_l_sue[alpha].append(Approximate_var_f_l(p1_sue, q1_sue, p2_sue, q2_sue))\n",
    "\n",
    "        # L-OSUE parameters\n",
    "        p1_oue = 1 / 2\n",
    "        q1_oue = 1 / (np.exp(eps_perm) + 1)\n",
    "\n",
    "        p2_oue = (1 - np.exp(eps_1 + eps_perm)) / (np.exp(eps_1) - np.exp(eps_perm) - np.exp(eps_1 + eps_perm) + 1)\n",
    "        q2_oue = 1 - p2_oue\n",
    "\n",
    "        var_l_osue[alpha].append(Approximate_var_f_l(p1_oue, q1_oue, p2_oue, q2_oue))\n",
    "\n",
    "        # OLOLOHA parameters\n",
    "        g = max(np.rint((np.sqrt(np.exp(4*eps_perm) - 14*np.exp(2*eps_perm) - 12*np.exp(2*eps_perm*(alpha+1)) + 12*np.exp(eps_perm*(alpha+1)) + 12*np.exp(eps_perm*(alpha+3)) + 1) - np.exp(2*eps_perm) + 6*np.exp(eps_perm) - 6*np.exp(eps_perm*alpha) + 1) / (6*(np.exp(eps_perm) - np.exp(eps_perm*alpha)))), 2)\n",
    "        p1_lh = np.exp(eps_perm) / (np.exp(eps_perm) + g - 1)\n",
    "        q1_lh = (1 - p1_lh) / (g-1)\n",
    "\n",
    "        p2_lh = (q1_lh - np.exp(eps_1) * p1_lh) / ((-p1_lh * np.exp(eps_1)) + g*q1_lh*np.exp(eps_1) - q1_lh*np.exp(eps_1) - p1_lh*(g-1)+q1_lh)\n",
    "        q2_lh = (1 - p2_lh) / (g-1)\n",
    "        q1_lh = 1 / g\n",
    "\n",
    "        var_ololoha[alpha].append(Approximate_var_f_l(p1_lh, q1_lh, p2_lh, q2_lh))\n",
    "\n",
    "        # BiLOLOHA parameters\n",
    "        g = 2\n",
    "        p1_lh = np.exp(eps_perm) / (np.exp(eps_perm) + g - 1)\n",
    "        q1_lh = (1 - p1_lh) / (g-1)\n",
    "\n",
    "        p2_lh = (q1_lh - np.exp(eps_1) * p1_lh) / ((-p1_lh * np.exp(eps_1)) + g*q1_lh*np.exp(eps_1) - q1_lh*np.exp(eps_1) - p1_lh*(g-1)+q1_lh)\n",
    "        q2_lh = (1 - p2_lh) / (g-1)\n",
    "        q1_lh = 1 / g\n",
    "\n",
    "        var_biloloha[alpha].append(Approximate_var_f_l(p1_lh, q1_lh, p2_lh, q2_lh))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reproducing Fig. 2 (variance values analysis) in paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "params = {'axes.titlesize':'20','xtick.labelsize':'20','ytick.labelsize':'20','font.size':'18','legend.fontsize':'medium','lines.linewidth':'2.5','font.weight':'normal','lines.markersize':'16','text.latex.preamble': r'\\usepackage{amsfonts}','lines.markerfacecolor':'none'}\n",
    "matplotlib.rcParams.update(params)\n",
    "plt.rcParams[\"mathtext.fontset\"] = \"cm\"\n",
    "plt.rc('text', usetex=True)\n",
    "plt.rc('font', family='serif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 3, figsize=(20, 7), sharey='row')\n",
    "plt.subplots_adjust(wspace=0.025, hspace=0.5)\n",
    "\n",
    "r = 0 #row\n",
    "c = 0 #column\n",
    "\n",
    "for alpha in lst_alpha:\n",
    "    ax[r, c].grid(color='grey', linestyle='dashdot', linewidth=0.5)    \n",
    "    ax[r, c].plot(var_l_osue[alpha],label='L-OSUE', marker='s', color='tab:orange')\n",
    "    ax[r, c].plot(var_ololoha[alpha],label='OLOLOHA', marker='d', color='tab:green')\n",
    "    ax[r, c].plot(var_l_sue[alpha],label='RAPPOR', marker='X', color='tab:red')\n",
    "    ax[r, c].plot(var_biloloha[alpha],label='BiLOLOHA', marker='o', color='tab:purple')    \n",
    "    \n",
    "    for k in lst_k:\n",
    "        ax[r, c].plot(var_l_grr[k][alpha],label='L-GRR[k='+str(k)+']', marker='*', color='tab:pink')\n",
    "    \n",
    "    ax[r, c].set_yscale('log')\n",
    "    ax[r, c].set_xticks(range(len(lst_eps)))\n",
    "    ax[r, c].set_xticklabels([round(val,4) for val in lst_eps])\n",
    "    ax[r, 0].set_ylabel(r'$\\mathbb{V}^*[f_{L}]$', fontsize=16)\n",
    "    ax[r, c].set_xlabel(\"$\\epsilon_{\\infty}$\", fontsize=16)\n",
    "    ax[r, c].set_title('$\\epsilon_1$ $=$' +f'${alpha}$'+ '$\\epsilon_{\\infty}$', loc='center')\n",
    "    ax[0, 1].legend(columnspacing=0.2, ncol=6, loc='upper center', bbox_to_anchor=(0.5, 1.45))\n",
    "    if c<2:\n",
    "        c+=1\n",
    "    else:\n",
    "        c=0\n",
    "        r += 1\n",
    "\n",
    "ax[0,0].set_ylim(1e-4, 3e-1)\n",
    "ax[1,0].set_ylim(1.5e-5, 2e-2)\n",
    "# plt.savefig('', dpi=500, bbox_inches = 'tight',pad_inches = 0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:root] *",
   "language": "python",
   "name": "conda-root-py"
  },
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