Пример #1
0
    def test_dp_sum_structure_list(self):
        query = privacy.GaussianSumQuery(5.0, 0.0)

        def _value_type_fn(value):
            del value
            return [tff.TensorType(tf.float32), tff.TensorType(tf.float32)]

        def _from_anon_tuple_fn(record):
            return list(record)

        dp_aggregate_fn, _ = differential_privacy.build_dp_aggregate(
            query,
            value_type_fn=_value_type_fn,
            from_anon_tuple_fn=_from_anon_tuple_fn)

        def datapoint(a, b):
            return [tf.Variable(a, name='a'), tf.Variable(b, name='b')]

        data = [
            datapoint(1.0, 2.0),
            datapoint(2.0, 3.0),
            datapoint(6.0, 8.0),  # Clipped to 3.0, 4.0
        ]

        initialize, aggregate = wrap_aggregate_fn(dp_aggregate_fn, data[0])
        global_state = initialize()

        global_state, result = aggregate(global_state, data)

        self.assertEqual(getattr(global_state, 'l2_norm_clip'), 5.0)
        self.assertEqual(getattr(global_state, 'stddev'), 0.0)

        result = list(result)
        self.assertEqual(result[0], 6.0)
        self.assertEqual(result[1], 9.0)
Пример #2
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    def test_dp_global_state_type(self):
        query = privacy.GaussianSumQuery(5.0, 0.0)

        _, dp_global_state_type = differential_privacy.build_dp_aggregate(
            query)

        self.assertEqual(dp_global_state_type.__class__.__name__,
                         'NamedTupleTypeWithPyContainerType')
Пример #3
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    def test_dp_sum(self):
        query = privacy.GaussianSumQuery(4.0, 0.0)

        dp_aggregate_fn, _ = differential_privacy.build_dp_aggregate(query)

        initialize, aggregate = wrap_aggregate_fn(dp_aggregate_fn, 0.0)
        global_state = initialize()

        global_state, result = aggregate(global_state, [1.0, 3.0, 5.0])

        self.assertEqual(getattr(global_state, 'l2_norm_clip'), 4.0)
        self.assertEqual(getattr(global_state, 'stddev'), 0.0)
        self.assertEqual(result, 8.0)
Пример #4
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    def test_dp_sum_structure_odict(self):
        query = privacy.GaussianSumQuery(5.0, 0.0)

        dp_aggregate_fn, _ = differential_privacy.build_dp_aggregate(query)

        def datapoint(a, b):
            return collections.OrderedDict([('a', (a, )), ('b', [b])])

        data = [
            datapoint(1.0, 2.0),
            datapoint(2.0, 3.0),
            datapoint(6.0, 8.0),  # Clipped to 3.0, 4.0
        ]

        initialize, aggregate = wrap_aggregate_fn(dp_aggregate_fn, data[0])
        global_state = initialize()

        global_state, result = aggregate(global_state, data)

        self.assertEqual(getattr(global_state, 'l2_norm_clip'), 5.0)
        self.assertEqual(getattr(global_state, 'stddev'), 0.0)

        self.assertEqual(getattr(result, 'a')[0], 6.0)
        self.assertEqual(getattr(result, 'b')[0], 9.0)