def passthought_leakage(subjects_data, tasks_list, times):
    '''Scenario 2: A subject's passthought was known and attackers try to impersonate him.'''

    log_file_name = 'Scenario2_log.txt'
    log = open(log_file_name, 'w')
    print("Scenario 2: Passthought Leakage")
    for target_subject in sorted(subjects_data.keys()):
        print('For subject' + str(target_subject) + ': ')
        logger = '{"' + target_subject + '":{'
        subject_sample_numbers = subjects_data[target_subject][1]

        for task in tasks_list:
            print('For task: ' + task)
            logger += '"' + task + '":{'
            task_sample_numbers = sum(subjects_data[target_subject][0][task][1])

            others_list = []
            others_subjects_sample_numbers = []
            others_sample_numbers = []
            for other_subject in sorted(subjects_data.keys()):
                if other_subject == target_subject:
                    continue
                else:
                    sample_numbers = sum(subjects_data[other_subject][0][task][1])
                    others_list.append(other_subject)
                    others_subjects_sample_numbers.append(sample_numbers)
                    others_sample_numbers.append([sample_numbers])

            FRR_holder = []
            FAR_holder = []

            for one_sample_run in range(times):
                logger += '"' + str(one_sample_run) + '":{'

                trainer, tester = selector.random_sampler(task_sample_numbers)
                logger += '"target":"' + str(trainer) + ',' + str(tester)
                train_data_target = selector.sample_mapper(trainer, [target_subject], [task], [subject_sample_numbers], [[task_sample_numbers]], subjects_data)
                test_data_target = selector.sample_mapper(tester, [target_subject], [task], [subject_sample_numbers], [[task_sample_numbers]], subjects_data)

                trainer, tester = selector.random_sampler(sum(others_subjects_sample_numbers))
                logger += '","other":"' + str(trainer) + ',' + str(tester) + '"}, '
                train_data_other = selector.sample_mapper(trainer, others_list, [task], others_subjects_sample_numbers, others_sample_numbers, subjects_data)
                test_data_other = selector.sample_mapper(tester, others_list, [task], others_subjects_sample_numbers, others_sample_numbers, subjects_data)

                X, y = learner.labeler([train_data_target, train_data_other])

                lin_clf = svm.LinearSVC()
                lin_clf.fit(X, y)

                test_result_correct = lin_clf.predict(test_data_target)
                FRR_holder.append(np.mean(test_result_correct))

                test_result_wrong = lin_clf.predict(test_data_other)
                FAR_holder.append(1 - np.mean(test_result_wrong))
            logger = logger[:-2] + '}, '
            print('  FRR = ' + str(np.mean(FRR_holder)) + ', FAR = ' + str(np.mean(FAR_holder)))
        logger = logger[:-2] + '}}\n'
        log.write(logger)
    log.close()
    return