privacy calculator

priv_calc.py

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+import tensorflow as tf
+from privacy_accountant import accountant, utils
+import argparse
+import time
+
+import pickle as pkl
+
+def calc_priv(noise, epochs, training_size, batch_size):
+    privacy_history = []
+    with tf.Session() as sess:
+        eps = tf.placeholder(tf.float32)
+        delta = tf.placeholder(tf.float32)
+
+        num_batches = epochs * (training_size / batch_size)
+        target_eps = [0.125,0.25,0.5,1,2,4,8]
+        priv_accountant = accountant.GaussianMomentsAccountant(training_size)
+
+        print('accum privacy, batches: ' + str(num_batches))
+        priv_start_time = time.clock()
+        privacy_accum_op = priv_accountant.accumulate_privacy_spending(
+          [None, None], args.noise, batch_size)
+        tf.global_variables_initializer().run()
+        for index in range(num_batches):
+            with tf.control_dependencies([privacy_accum_op]):
+                spent_eps_deltas = priv_accountant.get_privacy_spent(
+                    sess, target_eps=target_eps)
+                privacy_history.append(spent_eps_deltas)
+            sess.run([privacy_accum_op])
+
+        print('priv time: ', time.clock() - priv_start_time)
+
+        if spent_eps_deltas[-3][1] > 0.0001:
+            raise Exception('spent privacy')
+
+    pkl.dump(privacy_history, open('./privacy/' + str(noise) + '_' +
+             str(epochs) + '_' + str(training_size) + '_' + str(batch_size) +
+             '.p', 'wb'))
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--noise", type=float, default=8)
+    parser.add_argument("--epochs", type=int, default=500)
+    parser.add_argument("--training_size", type=int, default=6000)
+    parser.add_argument("--batch_size", type=int, default=1)
+    args = parser.parse_args()
+
+    calc_priv(args.noise, args.epochs, args.training_size, args.batch_size)