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Use the new probability distribution code in WTF-PAD.
Co-authored-by: Mike Perry <mikeperry-git@torproject.org> Co-authored-by: Taylor R Campbell <campbell+tor@mumble.net>
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@ -1,8 +1,11 @@
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/* Copyright (c) 2017 The Tor Project, Inc. */
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/* Copyright (c) 2017 The Tor Project, Inc. */
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/* See LICENSE for licensing information */
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/* See LICENSE for licensing information */
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#define CIRCUITPADDING_PRIVATE
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#include <math.h>
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#include <math.h>
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#include "lib/math/fp.h"
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#include "lib/math/fp.h"
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#include "lib/math/prob_distr.h"
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#include "core/or/or.h"
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#include "core/or/or.h"
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#include "core/or/circuitpadding.h"
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#include "core/or/circuitpadding.h"
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#include "core/or/circuitlist.h"
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#include "core/or/circuitlist.h"
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@ -374,7 +377,7 @@ circpad_distribution_sample_iat_delay(const circpad_state_t *state,
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* of tokens in each bin, and then a time value is chosen uniformly from
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* of tokens in each bin, and then a time value is chosen uniformly from
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* that bin's [start,end) time range.
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* that bin's [start,end) time range.
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*/
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*/
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static circpad_delay_t
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STATIC circpad_delay_t
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circpad_machine_sample_delay(circpad_machineinfo_t *mi)
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circpad_machine_sample_delay(circpad_machineinfo_t *mi)
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{
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{
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const circpad_state_t *state = circpad_machine_current_state(mi);
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const circpad_state_t *state = circpad_machine_current_state(mi);
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@ -487,57 +490,75 @@ circpad_machine_sample_delay(circpad_machineinfo_t *mi)
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static double
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static double
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circpad_distribution_sample(circpad_distribution_t dist)
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circpad_distribution_sample(circpad_distribution_t dist)
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{
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{
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double p = 0;
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log_fn(LOG_DEBUG,LD_CIRC, "Sampling delay with distribution %d",
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dist.type);
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switch (dist.type) {
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switch (dist.type) {
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case CIRCPAD_DIST_NONE:
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case CIRCPAD_DIST_NONE:
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return 0;
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{
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case CIRCPAD_DIST_UNIFORM:
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/* We should not get in here like this */
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p = crypto_rand_double();
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tor_assert_nonfatal_unreached();
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// param2 is upper bound, param1 is lower
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/* The subtraction is exact as long as param2 and param1 are less than
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* 2**53. The multiplication is accurate as long as (param2 - param1)
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* is less than 2**52. (And when they are large, the low bits aren't
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* important.) The result covers the full range of outputs, as long as
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* p has a resolution of 1/2**32 or greater. */
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p *= (dist.param2 - dist.param1);
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p += dist.param1;
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return p;
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case CIRCPAD_DIST_LOGISTIC:
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p = crypto_rand_double();
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/* https://en.wikipedia.org/wiki/Logistic_distribution#Quantile_function
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* param1 is Mu, param2 is s. */
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if (p <= 0.0) // Avoid log(0)
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return 0;
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return 0;
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return dist.param1 + dist.param2*tor_mathlog(p/(1.0-p));
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}
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case CIRCPAD_DIST_UNIFORM:
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{
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// param2 is upper bound, param1 is lower
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const struct uniform my_uniform = {
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.base = DIST_BASE(&uniform_ops),
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.a = dist.param1,
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.b = dist.param2,
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};
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return uniform_sample(&my_uniform.base);
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}
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case CIRCPAD_DIST_LOGISTIC:
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{
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/* param1 is Mu, param2 is sigma. */
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const struct logistic my_logistic = {
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.base = DIST_BASE(&uniform_ops),
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.mu = dist.param1,
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.sigma = dist.param2,
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};
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return logistic_sample(&my_logistic.base);
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}
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case CIRCPAD_DIST_LOG_LOGISTIC:
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case CIRCPAD_DIST_LOG_LOGISTIC:
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p = crypto_rand_double();
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{
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/* https://en.wikipedia.org/wiki/Log-logistic_distribution#Quantiles
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/* param1 is Alpha, param2 is 1.0/Beta */
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* param1 is Alpha, param2 is Beta */
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const struct log_logistic my_log_logistic = {
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return dist.param1 * pow(p/(1.0-p), 1.0/dist.param2);
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.base = DIST_BASE(&log_logistic_ops),
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.alpha = dist.param1,
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.beta = dist.param2,
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};
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return log_logistic_sample(&my_log_logistic.base);
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}
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case CIRCPAD_DIST_GEOMETRIC:
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case CIRCPAD_DIST_GEOMETRIC:
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{
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{
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/* param1 is 'p' (success probability) */
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/* param1 is 'p' (success probability) */
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return geometric_sample(dist.param1);
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return geometric_sample(dist.param1);
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}
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}
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case CIRCPAD_DIST_WEIBULL:
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case CIRCPAD_DIST_WEIBULL:
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p = crypto_rand_double();
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{
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/* https://en.wikipedia.org/wiki/Weibull_distribution \
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/* param1 is k, param2 is Lambda */
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* #Cumulative_distribution_function
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const struct weibull my_weibull = {
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* param1 is k, param2 is Lambda */
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.base = DIST_BASE(&weibull_ops),
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return dist.param2*pow(-tor_mathlog(1.0-p), 1.0/dist.param1);
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.k = dist.param1,
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.lambda = dist.param2,
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};
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return weibull_sample(&my_weibull.base);
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}
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case CIRCPAD_DIST_PARETO:
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case CIRCPAD_DIST_PARETO:
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p = 1.0-crypto_rand_double(); // Pareto quantile needs (0,1]
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{
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/* param1 is sigma, param2 is xi, no more params for mu so we use 0 */
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/* https://en.wikipedia.org/wiki/Generalized_Pareto_distribution \
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const struct genpareto my_genpareto = {
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* #Generating_generalized_Pareto_random_variables
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.base = DIST_BASE(&weibull_ops),
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* param1 is Sigma, param2 is Xi
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.mu = 0,
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* Since it's piecewise, we must define it for 0 (or close to 0) */
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.sigma = dist.param1,
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if (fabs(dist.param2) <= 1e-22)
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.xi = dist.param2,
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return -dist.param1*tor_mathlog(p);
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};
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else
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return genpareto_sample(&my_genpareto.base);
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return dist.param1*(pow(p, -dist.param2) - 1.0)/dist.param2;
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}
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}
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}
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tor_assert_nonfatal_unreached();
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return 0;
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return 0;
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}
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}
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@ -1086,8 +1107,8 @@ circpad_machine_reached_padding_limit(circpad_machineinfo_t *mi)
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* Returns 1 if we decide to transition states (due to infinity bin),
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* Returns 1 if we decide to transition states (due to infinity bin),
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* 0 otherwise.
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* 0 otherwise.
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*/
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*/
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circpad_decision_t
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MOCK_IMPL(circpad_decision_t,
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circpad_machine_schedule_padding(circpad_machineinfo_t *mi)
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circpad_machine_schedule_padding,(circpad_machineinfo_t *mi))
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{
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{
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circpad_delay_t in_usec = 0;
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circpad_delay_t in_usec = 0;
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struct timeval timeout;
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struct timeval timeout;
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