commit 71fb889aaf099fa99c53f3dcb87dff09aec77814 Author: Mike Perry <mikeperry-git@fscked.org> Date: Wed Oct 10 14:55:11 2012 -0700 Represent false positives as raw counts. Also, clarify the startup false positive test. --- CircuitAnalysis/PathBias/path_bias.py | 67 ++++++++++++++++++++------------- 1 files changed, 41 insertions(+), 26 deletions(-) diff --git a/CircuitAnalysis/PathBias/path_bias.py b/CircuitAnalysis/PathBias/path_bias.py index 621e2c1..2487bc9 100755 --- a/CircuitAnalysis/PathBias/path_bias.py +++ b/CircuitAnalysis/PathBias/path_bias.py @@ -15,7 +15,7 @@ PATH_BIAS_MIN_CIRCS = 20 # XXX: An int divisor was wrong here. Fix that in Tor. We might # even want a weighted moving average, but that will be trickier # to analyze. -PATH_BIAS_SCALE_FACTOR = 95 +PATH_BIAS_SCALE_FACTOR = 50 PATH_BIAS_SCALE_THRESHOLD = 250 # XXX: We should only emit warnings if we are above the scaling threshhold.. @@ -33,7 +33,7 @@ def reset_globals(): PATH_BIAS_PCT = 70 PATH_BIAS_MIN_CIRCS = 20 - PATH_BIAS_SCALE_FACTOR = 95 + PATH_BIAS_SCALE_FACTOR = 50 PATH_BIAS_SCALE_THRESHOLD = 250 PATH_BIAS_WARN_CIRCS = PATH_BIAS_SCALE_THRESHOLD*(PATH_BIAS_SCALE_FACTOR/100.0) @@ -294,7 +294,7 @@ def simulate_circs_until(g, circ_count, say_when): # success_rate # PATH_BIAS_MIN_CIRCS = 20 # PATH_BIAS_PCT = 70 -def notice_false_positive_test(trials, success_rate, min_circs, path_bias_pct): +def startup_false_positive_test(trials, success_rate, min_circs, path_bias_pct): # FIXME: Look it's just easier this way, ok? Get off my back already global PATH_BIAS_MIN_CIRCS global PATH_BIAS_PCT @@ -309,7 +309,7 @@ def notice_false_positive_test(trials, success_rate, min_circs, path_bias_pct): #print g._get_rate() - return g.reject_rate() + return g.rejected_count def reject_false_positive_test(trials, success_rate, scale_circs, path_bias_pct): # FIXME: Look it's just easier this way, ok? Get off my back already @@ -327,9 +327,7 @@ def reject_false_positive_test(trials, success_rate, scale_circs, path_bias_pct) simulate_circs_until(g, trials, lambda g: False) - #print g._get_rate() - - return g.reject_rate() + return g.rejected_count def generic_rate_test(g, trials, success_rate, adversary_capacity, path_bias_pct, rate_fcn): # FIXME: Look it's just easier this way, ok? Get off my back already @@ -501,52 +499,69 @@ def main(): if True: print "\n\n===================== FALSE POSITIVES ============================" - print "\nNotice false positive rates at [trials, success_rate, min_circs, path_bias_pct]:" + print "\nStartup false positive counts at [trials, success_rate, min_circs, path_bias_pct]:" print "(Results are some function of success_rate - path_bias_pct vs min_circs)" print brute_force(lambda x,y: x<y, - notice_false_positive_test, + startup_false_positive_test, #false_positive_test(trials, success_rate, min_circs, path_bias_pct): - [(100000,100000), (0.65, 0.65), (50,250), (70, 70)], - [0, -0.1, 50, 5]) + [(100000,100000), (0.80, 0.80), (20,200), (70, 70)], + [0, -0.1, 20, 5]) - print "\nNotice false positive rates at [trials, success_rate, min_circs, path_bias_pct]:" + print "\nStartup false positive counts at [trials, success_rate, min_circs, path_bias_pct]:" print "(Results are some function of success_rate - path_bias_pct vs min_circs)" print brute_force(lambda x,y: x<y, - notice_false_positive_test, + startup_false_positive_test, #false_positive_test(trials, success_rate, min_circs, path_bias_pct): - [(100000,100000), (0.70, 0.70), (50,500), (70, 70)], + [(100000,100000), (0.45, 0.45), (20,200), (30, 30)], + [0, -0.1, 20, 5]) + + + print "\nFalse positive counts at [trials, success_rate, scale_circs, path_bias_pct]:" + print "(Results are some function of success_rate - path_bias_pct vs scale_circs)" + print brute_force(lambda x,y: x<y, + reject_false_positive_test, + #false_positive_test(trials, success_rate, scale_circs, path_bias_pct): + [(1000000,1000000), (0.70, 0.70), (100,500), (70, 70)], [0, -0.1, 50, 5]) - print "\nNotice false positives at [trials, success_rate, min_circs, path_bias_pct]:" - print "(Results are some function of success_rate - path_bias_pct vs min_circs)" + print "\nFalse positive counts at [trials, success_rate, scale_circs, path_bias_pct]:" + print "(Results are some function of success_rate - path_bias_pct vs scale_circs)" print brute_force(lambda x,y: x<y, - notice_false_positive_test, - #false_positive_test(trials, success_rate, min_circs, path_bias_pct): - [(100000,100000), (0.75, 0.75), (20,400), (70, 70)], - [0, -0.1, 20, 5]) + reject_false_positive_test, + #false_positive_test(trials, success_rate, scale_circs, path_bias_pct): + [(1000000,1000000), (0.75, 0.75), (100,500), (70, 70)], + [0, -0.1, 50, 5]) + + print "\nFalse positive counts at [trials, success_rate, scale_circs, path_bias_pct]:" + print "(Results are some function of success_rate - path_bias_pct vs scale_circs)" + print brute_force(lambda x,y: x<y, + reject_false_positive_test, + #false_positive_test(trials, success_rate, scale_circs, path_bias_pct): + [(1000000,1000000), (0.80, 0.80), (100,500), (70, 70)], + [0, -0.1, 50, 5]) - print "\nReject false positive rates at [trials, success_rate, scale_circs, path_bias_pct]:" + print "\nFalse positive counts at [trials, success_rate, scale_circs, path_bias_pct]:" print "(Results are some function of success_rate - path_bias_pct vs scale_circs)" print brute_force(lambda x,y: x<y, reject_false_positive_test, #false_positive_test(trials, success_rate, scale_circs, path_bias_pct): - [(1000000,1000000), (0.65, 0.65), (50,250), (70, 70)], + [(1000000,1000000), (0.55, 0.55), (100,500), (50, 50)], [0, -0.1, 50, 5]) - print "\nReject false positive rates at [trials, success_rate, scale_circs, path_bias_pct]:" + print "\nFalse positive counts at [trials, success_rate, scale_circs, path_bias_pct]:" print "(Results are some function of success_rate - path_bias_pct vs scale_circs)" print brute_force(lambda x,y: x<y, reject_false_positive_test, #false_positive_test(trials, success_rate, scale_circs, path_bias_pct): - [(1000000,1000000), (0.70, 0.70), (50,500), (70, 70)], + [(1000000,1000000), (0.60, 0.60), (100,500), (50, 50)], [0, -0.1, 50, 5]) - print "\nReject false positive rates at [trials, success_rate, scale_circs, path_bias_pct]:" + print "\nFalse positive counts at [trials, success_rate, scale_circs, path_bias_pct]:" print "(Results are some function of success_rate - path_bias_pct vs scale_circs)" print brute_force(lambda x,y: x<y, reject_false_positive_test, #false_positive_test(trials, success_rate, scale_circs, path_bias_pct): - [(1000000,1000000), (0.75, 0.75), (50,500), (70, 70)], + [(1000000,1000000), (0.45, 0.45), (100,500), (30, 30)], [0, -0.1, 50, 5])