Fork of the official github repository of the framework Leaky-LWE-Estimator, a Sage Toolkit to attack and estimate the hardness of LWE with Side Information. https://github.com/lducas/leaky-LWE-Estimator
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aposteriori_distribution_NIST2.sage 2.8KB

4 years ago
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  1. variance_aposteriori = {}
  2. center_aposteriori = {}
  3. proba_best_guess_correct = {}
  4. nb_samples_for_measure = 13205
  5. variance_aposteriori[0] = 0.0
  6. center_aposteriori[0] = 0.0
  7. proba_best_guess_correct[0] = 1.0
  8. nb_samples_for_measure = 15203
  9. variance_aposteriori[1] = 0.21798458216693134
  10. center_aposteriori[1] = 1.2924422811287246
  11. proba_best_guess_correct[1] = 0.7091363546668421
  12. nb_samples_for_measure = 6352
  13. variance_aposteriori[2] = 0.3064210320911798
  14. center_aposteriori[2] = 1.8000629722921915
  15. proba_best_guess_correct[2] = 0.7762909319899244
  16. nb_samples_for_measure = 5910
  17. variance_aposteriori[3] = 0.16892755007632676
  18. center_aposteriori[3] = 3.0253807106598987
  19. proba_best_guess_correct[3] = 0.9956006768189509
  20. nb_samples_for_measure = 3060
  21. variance_aposteriori[4] = 0.027589327111470077
  22. center_aposteriori[4] = 4.014052287581699
  23. proba_best_guess_correct[4] = 0.9928104575163399
  24. nb_samples_for_measure = 1364
  25. variance_aposteriori[5] = 0.24318176439327602
  26. center_aposteriori[5] = 5.2353372434017595
  27. proba_best_guess_correct[5] = 0.7961876832844574
  28. nb_samples_for_measure = 524
  29. variance_aposteriori[6] = 0.3345350517420052
  30. center_aposteriori[6] = 5.412213740458015
  31. proba_best_guess_correct[6] = 0.4580152671755725
  32. nb_samples_for_measure = 101
  33. variance_aposteriori[7] = 0.07841584158415837
  34. center_aposteriori[7] = 6.96039603960396
  35. proba_best_guess_correct[7] = 0.9801980198019802
  36. nb_samples_for_measure = 0
  37. variance_aposteriori[8] = None
  38. center_aposteriori[8] = 0
  39. nb_samples_for_measure = 0
  40. variance_aposteriori[9] = None
  41. center_aposteriori[9] = 0
  42. nb_samples_for_measure = 0
  43. variance_aposteriori[10] = None
  44. center_aposteriori[10] = 0
  45. nb_samples_for_measure = 0
  46. variance_aposteriori[-10] = None
  47. center_aposteriori[-10] = 0
  48. nb_samples_for_measure = 12
  49. variance_aposteriori[-9] = 0.0
  50. center_aposteriori[-9] = -9.0
  51. proba_best_guess_correct[-9] = 1.0
  52. nb_samples_for_measure = 43
  53. variance_aposteriori[-8] = 0.0
  54. center_aposteriori[-8] = -8.0
  55. proba_best_guess_correct[-8] = 1.0
  56. nb_samples_for_measure = 0
  57. variance_aposteriori[-7] = None
  58. center_aposteriori[-7] = 0
  59. nb_samples_for_measure = 2
  60. variance_aposteriori[-6] = None
  61. center_aposteriori[-6] = 0
  62. nb_samples_for_measure = 1639
  63. variance_aposteriori[-5] = 3.5637539194586174
  64. center_aposteriori[-5] = -2.611348383157747
  65. proba_best_guess_correct[-5] = 0.3776693105552166
  66. nb_samples_for_measure = 3598
  67. variance_aposteriori[-4] = 0.658169452247202
  68. center_aposteriori[-4] = -4.334908282377
  69. proba_best_guess_correct[-4] = 0.8390772651473041
  70. nb_samples_for_measure = 0
  71. variance_aposteriori[-3] = None
  72. center_aposteriori[-3] = 0
  73. nb_samples_for_measure = 15289
  74. variance_aposteriori[-2] = 0.39810522738958687
  75. center_aposteriori[-2] = -2.4475766891264357
  76. proba_best_guess_correct[-2] = 0.5968997318333442
  77. nb_samples_for_measure = 11778
  78. variance_aposteriori[-1] = 0.3925327006787841
  79. center_aposteriori[-1] = -1.0894039735067054
  80. proba_best_guess_correct[-1] = 0.978689081338088