1 files changed, 137 insertions, 87 deletions

diff --git a/util/plot_dsa.ipynb b/util/plot_dsa.ipynb
index dbfb38b..503bde8 100644
--- a/util/plot_dsa.ipynb
+++ b/util/plot_dsa.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Analysis of key generation data"
+    "# Analysis of signature data"
    ]
   },
   {
@@ -12,13 +12,13 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-03-17T23:00:25.518989Z",
-     "start_time": "2019-03-17T23:00:24.501601Z"
+     "end_time": "2019-03-18T18:08:10.526799Z",
+     "start_time": "2019-03-18T18:08:10.073972Z"
     }
    },
    "outputs": [],
    "source": [
-    " %matplotlib notebook \n",
+    "%matplotlib notebook\n",
     "import numpy as np\n",
     "from scipy.stats import describe\n",
     "from scipy.stats import norm as norm_dist\n",
@@ -27,7 +27,7 @@
     "import matplotlib.pyplot as plt\n",
     "from matplotlib import ticker, colors, gridspec\n",
     "from copy import deepcopy\n",
-    "from utils import plot_hist, moving_average, hw\n",
+    "from utils import plot_hist, moving_average, hw, time_scale\n",
     "from binascii import unhexlify\n",
     "from IPython.display import display, HTML\n",
     "from ipywidgets import interact, interactive, fixed, interact_manual\n",
@@ -48,8 +48,8 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-03-17T23:06:29.704432Z",
-     "start_time": "2019-03-17T23:06:29.694540Z"
+     "end_time": "2019-03-18T18:15:54.067732Z",
+     "start_time": "2019-03-18T18:15:54.063679Z"
     }
    },
    "outputs": [],
@@ -57,8 +57,17 @@
     "# File name with output from ECTesterReader or ECTesterStandalone signatures.\n",
     "fname = \"filename.csv\"\n",
     "\n",
+    "# The time unit used in displaying the plots. One of \"milli\", \"micro\", \"nano\".\n",
+    "# WARNING: Using nano might lead to very large plots/histograms and to the\n",
+    "#          notebook to freeze or run out of memory, as well as bad visualization\n",
+    "#          quality, due to noise and low density.\n",
+    "sign_unit = \"milli\"\n",
+    "verify_unit = \"milli\"\n",
+    "# A number which will be used to divide the time into sub-units, e.g. for 5, time will be in fifths of units\n",
+    "scaling_factor = 1\n",
+    "\n",
     "# The amount of entries skipped from the beginning of the file, as they are usually outliers.\n",
-    "skip_first = 10\n",
+    "skip_first = 100\n",
     "\n",
     "# Whether to plot things in logarithmic scale or not.\n",
     "log_scale = False\n",
@@ -94,8 +103,8 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-03-17T23:06:30.551732Z",
-     "start_time": "2019-03-17T23:06:30.545202Z"
+     "end_time": "2019-03-18T18:15:55.985799Z",
+     "start_time": "2019-03-18T18:15:55.495414Z"
     }
    },
    "outputs": [],
@@ -111,20 +120,8 @@
     "if log_scale:\n",
     "    norm = colors.LogNorm()\n",
     "else:\n",
-    "    norm = colors.Normalize()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2019-03-17T23:00:38.023486Z",
-     "start_time": "2019-03-17T23:00:27.178465Z"
-    }
-   },
-   "outputs": [],
-   "source": [
+    "    norm = colors.Normalize()\n",
+    "\n",
     "# Read the header line.\n",
     "\n",
     "with open(fname, \"r\") as f:\n",
@@ -142,39 +139,18 @@
     "                                                                      8: lambda b: bool(int(b))},\n",
     "                     dtype=np.dtype([(\"index\", \"u4\"), (\"sign_time\", \"u4\"), (\"verify_time\", \"u4\"),\n",
     "                                     (\"data\", \"O\"), (\"pub\", \"O\"), (\"priv\", \"O\"), (\"signature\", \"O\"),\n",
-    "                                    (\"nonce\", \"O\"), (\"valid\", \"b\")]))\n",
-    "\n",
-    "    \n",
-    "sign_unit = \"ms\"\n",
-    "verify_unit = \"ms\"\n",
-    "# Setup the datatrimmed = False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2019-03-17T23:00:38.465677Z",
-     "start_time": "2019-03-17T23:00:38.025692Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Setup the data\n",
-    "\n",
+    "                                     (\"nonce\", \"O\"), (\"valid\", \"b\")]))\n",
     "# Skip first (outliers?)\n",
     "\n",
     "data = data[skip_first:]\n",
     "\n",
-    "# If in nanoseconds, scale to microseconds\n",
-    "if header_names[1].endswith(\"[nano]\") and sign_unit == \"ms\":\n",
-    "    sign_unit = r\"$\\mu s$\"\n",
-    "    np.floor_divide(data[\"sign_time\"], 1000, out=data[\"sign_time\"])\n",
+    "# Setup the data\n",
     "\n",
-    "if header_names[2].endswith(\"[nano]\") and verify_unit == \"ms\":\n",
-    "    verify_unit = r\"$\\mu s$\"\n",
-    "    np.floor_divide(data[\"verify_time\"], 1000, out=data[\"verify_time\"])\n",
+    "# Convert time data\n",
+    "orig_sign_unit = header_names[1].split(\"[\")[1][:-1]\n",
+    "orig_verify_unit = header_names[2].split(\"[\")[1][:-1]\n",
+    "sign_disp_unit = time_scale(data[\"sign_time\"], orig_sign_unit, sign_unit, scaling_factor)\n",
+    "verify_disp_unit = time_scale(data[\"verify_time\"], orig_verify_unit, verify_unit, scaling_factor)\n",
     "\n",
     "# Trim times\n",
     "quant_low_bound = trim_low if 0 <= trim_low <= 1 else 0.01\n",
@@ -242,8 +218,8 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-03-17T23:00:39.540701Z",
-     "start_time": "2019-03-17T23:00:39.511019Z"
+     "end_time": "2019-03-18T18:15:57.175564Z",
+     "start_time": "2019-03-18T18:15:57.161611Z"
     }
    },
    "outputs": [],
@@ -270,14 +246,14 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-03-17T23:00:40.974497Z",
-     "start_time": "2019-03-17T23:00:40.953755Z"
+     "end_time": "2019-03-18T18:15:58.257820Z",
+     "start_time": "2019-03-18T18:15:58.254036Z"
     }
    },
    "outputs": [],
    "source": [
     "tbl = [(quant_low_bound, \"0.25\", \"0.5\", \"0.75\", quant_high_bound),\n",
-    "       list(map(lambda x: \"{} {}\".format(x, sign_unit), quantiles_sign))]\n",
+    "       list(map(lambda x: \"{} {}\".format(x, sign_disp_unit), quantiles_sign))]\n",
     "display(HTML(tabulate.tabulate(tbl, tablefmt=\"html\")))"
    ]
   },
@@ -293,8 +269,8 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-03-17T23:00:41.961541Z",
-     "start_time": "2019-03-17T23:00:41.949385Z"
+     "end_time": "2019-03-18T18:15:58.917927Z",
+     "start_time": "2019-03-18T18:15:58.909693Z"
     }
    },
    "outputs": [],
@@ -313,7 +289,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Nonce MSB vs signature time heatmap"
+    "### Nonce MSB vs signature time heatmap\n",
+    "The heatmap should show uncorrelated variables."
    ]
   },
   {
@@ -321,8 +298,8 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-03-17T23:06:34.030472Z",
-     "start_time": "2019-03-17T23:06:33.761991Z"
+     "end_time": "2019-03-18T18:15:59.977656Z",
+     "start_time": "2019-03-18T18:15:59.926337Z"
     }
    },
    "outputs": [],
@@ -338,7 +315,7 @@
     "im = axe_nonce.imshow(heatmap.T, extent=extent, aspect=\"auto\", cmap=cmap, origin=\"low\",\n",
     "                   interpolation=\"nearest\", norm=norm)\n",
     "axe_nonce.set_xlabel(\"nonce key MSB value\")\n",
-    "axe_nonce.set_ylabel(\"signature time ({})\".format(sign_unit))\n",
+    "axe_nonce.set_ylabel(\"signature time ({})\".format(sign_disp_unit))\n",
     "fig_nonce.colorbar(im, ax=axe_nonce)\n",
     "\n",
     "del nonce_msb"
@@ -348,7 +325,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Nonce Hamming Weight vs signature time heatmap"
+    "### Nonce Hamming Weight vs signature time heatmap\n",
+    "The heatmap should show uncorrelated variables.\n",
+    "\n",
+    "Also contains a nonce Hamming Weight histogram, which should be binomially distributed."
    ]
   },
   {
@@ -356,8 +336,8 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-03-17T23:05:26.804859Z",
-     "start_time": "2019-03-17T23:05:18.214110Z"
+     "end_time": "2019-03-18T18:16:01.977710Z",
+     "start_time": "2019-03-18T18:16:01.717704Z"
     }
    },
    "outputs": [],
@@ -371,7 +351,7 @@
     "im = axe_nonce_hist.imshow(h.T, origin=\"low\", cmap=cmap, aspect=\"auto\", extent=[xe[0], xe[-1], ye[0], ye[-1]], norm=norm)\n",
     "axe_nonce_hist.axvline(x=bit_size//2, alpha=0.7, linestyle=\"dotted\", color=\"white\", label=str(bit_size//2) + \" bits\")\n",
     "axe_nonce_hist.set_xlabel(\"nonce Hamming weight\")\n",
-    "axe_nonce_hist.set_ylabel(\"signature time ({})\".format(sign_unit))\n",
+    "axe_nonce_hist.set_ylabel(\"signature time ({})\".format(sign_disp_unit))\n",
     "axe_nonce_hist.legend(loc=\"best\")\n",
     "\n",
     "plot_hist(axe_nonce_hist_hw, nonce_hw, \"nonce Hamming weight\", log_scale, True, True)\n",
@@ -401,8 +381,8 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-03-17T23:05:32.395983Z",
-     "start_time": "2019-03-17T23:05:32.068823Z"
+     "end_time": "2019-03-18T18:16:03.232728Z",
+     "start_time": "2019-03-18T18:16:03.134237Z"
     }
    },
    "outputs": [],
@@ -410,8 +390,8 @@
     "fig_sig_hist = plt.figure(figsize=(10.5, 8), dpi=90)\n",
     "axe_hist_full = fig_sig_hist.add_subplot(2, 1, 1)\n",
     "axe_hist_trim = fig_sig_hist.add_subplot(2, 1, 2)\n",
-    "plot_hist(axe_hist_full, data[\"sign_time\"], \"signature time ({})\".format(sign_unit), log_scale, hist_size_sign_time);\n",
-    "plot_hist(axe_hist_trim, data_trimmed[\"sign_time\"], \"signature time ({})\".format(sign_unit), log_scale, hist_size_sign_time_trim);"
+    "plot_hist(axe_hist_full, data[\"sign_time\"], \"signature time ({})\".format(sign_disp_unit), log_scale, hist_size_sign_time);\n",
+    "plot_hist(axe_hist_trim, data_trimmed[\"sign_time\"], \"signature time ({})\".format(sign_disp_unit), log_scale, hist_size_sign_time_trim);"
    ]
   },
   {
@@ -426,15 +406,15 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-03-17T23:05:33.358613Z",
-     "start_time": "2019-03-17T23:05:32.963791Z"
+     "end_time": "2019-03-18T18:16:04.380116Z",
+     "start_time": "2019-03-18T18:16:04.227481Z"
     }
    },
    "outputs": [],
    "source": [
     "fig_vrfy_hist = plt.figure(figsize=(10.5, 5), dpi=90)\n",
     "axe_hist_full = fig_vrfy_hist.add_subplot(1, 1, 1)\n",
-    "plot_hist(axe_hist_full, data[\"verify_time\"], \"verification time ({})\".format(verify_unit), log_scale, hist_size_sign_time);"
+    "plot_hist(axe_hist_full, data[\"verify_time\"], \"verification time ({})\".format(verify_disp_unit), log_scale, hist_size_sign_time);"
    ]
   },
   {
@@ -449,8 +429,8 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-03-17T23:05:33.971385Z",
-     "start_time": "2019-03-17T23:05:33.732857Z"
+     "end_time": "2019-03-18T18:16:05.236199Z",
+     "start_time": "2019-03-18T18:16:05.123540Z"
     }
    },
    "outputs": [],
@@ -466,7 +446,7 @@
     "    axe_sign_avg.axhline(y=low_bound, alpha=0.7, linestyle=\"dotted\", color=\"green\", label=\"Low trim bound = {}\".format(low_bound))\n",
     "if high_bound is not None:\n",
     "    axe_sign_avg.axhline(y=high_bound, alpha=0.7, linestyle=\"dotted\", color=\"orange\", label=\"Hight trim bound = {}\".format(high_bound))\n",
-    "axe_sign_avg.set_ylabel(\"signature time ({})\".format(sign_unit))\n",
+    "axe_sign_avg.set_ylabel(\"signature time ({})\".format(sign_disp_unit))\n",
     "axe_sign_avg.set_xlabel(\"index\")\n",
     "axe_sign_avg.legend(loc=\"best\")\n",
     "\n",
@@ -474,7 +454,7 @@
     "avg_vrfy_1000 = moving_average(data[\"verify_time\"], 1000)\n",
     "axe_vrfy_avg.plot(avg_vrfy_100, label=\"window = 100\")\n",
     "axe_vrfy_avg.plot(avg_vrfy_1000, label=\"window = 1000\")\n",
-    "axe_vrfy_avg.set_ylabel(\"verification time ({})\".format(verify_unit))\n",
+    "axe_vrfy_avg.set_ylabel(\"verification time ({})\".format(verify_disp_unit))\n",
     "axe_vrfy_avg.set_xlabel(\"index\")\n",
     "axe_vrfy_avg.legend(loc=\"best\")\n",
     "\n",
@@ -485,7 +465,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Nonce MSB and LSB histograms"
+    "### Nonce MSB and LSB histograms\n",
+    "Expected to be uniform over [0, 255]."
    ]
   },
   {
@@ -493,8 +474,8 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-03-17T23:05:36.256032Z",
-     "start_time": "2019-03-17T23:05:35.302194Z"
+     "end_time": "2019-03-18T18:16:06.352067Z",
+     "start_time": "2019-03-18T18:16:06.059476Z"
     }
    },
    "outputs": [],
@@ -514,7 +495,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Nonce bit length histogram"
+    "### Nonce bit length vs signature time heatmap\n",
+    "Also contains nonce bit length histogram, which is expected to be axis flipped geometric distribution with $p = \\frac{1}{2}$ peaking at the bit size of the order of the curve."
    ]
   },
   {
@@ -522,8 +504,8 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-03-17T23:05:45.320760Z",
-     "start_time": "2019-03-17T23:05:44.951189Z"
+     "end_time": "2019-03-18T18:16:07.625289Z",
+     "start_time": "2019-03-18T18:16:07.544334Z"
     }
    },
    "outputs": [],
@@ -537,7 +519,7 @@
     "h, xe, ye = np.histogram2d(bl_data, data_trimmed[\"sign_time\"], bins=[max(bl_data) - min(bl_data), hist_size_sign_time_trim])\n",
     "im = axe_bl_heat.imshow(h.T, origin=\"low\", cmap=cmap, aspect=\"auto\", extent=[xe[0], xe[-1], ye[0], ye[-1]], norm=norm)\n",
     "axe_bl_heat.set_xlabel(\"nonce bit length\")\n",
-    "axe_bl_heat.set_ylabel(\"signature time ({})\".format(sign_unit))\n",
+    "axe_bl_heat.set_ylabel(\"signature time ({})\".format(sign_disp_unit))\n",
     "\n",
     "plot_hist(axe_bl_hist, bl_data, \"nonce bit length\", log_scale, align=\"right\")\n",
     "fig_bl.colorbar(im, ax=[axe_bl_heat, axe_bl_hist])\n",
@@ -546,6 +528,74 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Validation\n",
+    "Perform some tests on the produced data and compare to expected results.\n",
+    "\n",
+    "This requires some information about the used curve, enter it below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-03-18T18:16:48.791656Z",
+     "start_time": "2019-03-18T18:16:45.435426Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "p_str = input(\"The prime specifying the finite field:\")\n",
+    "p = int(p_str, 16) if p_str.startswith(\"0x\") else int(p_str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-03-18T18:16:55.343989Z",
+     "start_time": "2019-03-18T18:16:49.543154Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "r_str = input(\"The order of the curve:\")\n",
+    "r = int(r_str, 16) if r_str.startswith(\"0x\") else int(r_str)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All of the following tests should pass (e.g. be true):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-03-18T18:16:56.289305Z",
+     "start_time": "2019-03-18T18:16:56.278296Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "max_priv = max(data[\"priv\"])\n",
+    "max_nonce = max(data[\"nonce\"])\n",
+    "display(max_priv < r)\n",
+    "display(r <= p or max_priv > p)\n",
+    "display(max_nonce < r)\n",
+    "display(r <= p or max_nonce > p)\n",
+    "display(max_priv.bit_length() == r.bit_length())\n",
+    "display(max_nonce.bit_length() == r.bit_length())"
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
@@ -555,8 +605,8 @@
  ],
  "metadata": {
   "@webio": {
-   "lastCommId": "2218f80b2f784436bce6ffed5d971ea3",
-   "lastKernelId": "1c06331a-17dd-4743-9e31-832000e597c8"
+   "lastCommId": "7c4c5d836a8d43e5846df95890bbafa3",
+   "lastKernelId": "b01f6c07-c08b-4348-a503-dc2c9cf1db89"
   },
   "hide_input": false,
   "kernelspec": {