tf_privacy.NoPrivacyQuantileEstimatorQuery

Iterative process to estimate target quantile of a univariate distribution.

Inherits From: QuantileEstimatorQuery, SumAggregationDPQuery, DPQuery

tf_privacy.NoPrivacyQuantileEstimatorQuery(
    initial_estimate, target_quantile, learning_rate, geometric_update=False
)

Unlike the base class, this uses a NoPrivacyQuery to estimate the fraction below estimate with an exact denominator, so there are no privacy guarantees.

initial_estimate The initial estimate of the quantile.
target_quantile The target quantile. I.e., a value of 0.8 means a value should be found for which approximately 80% of updates are less than the estimate each round.
learning_rate The learning rate. A rate of r means that the estimate will change by a maximum of r at each step (for arithmetic updating) or by a maximum factor of exp(r) (for geometric updating). Andrew et al. recommends that this be set to 0.2 for geometric updating.
geometric_update If True, use geometric updating of estimate. Geometric updating is preferred for non-negative records like vector norms that could potentially be very large or very close to zero.

Methods

accumulate_preprocessed_record

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accumulate_preprocessed_record(
    sample_state, preprocessed_record
)

Implements tensorflow_privacy.DPQuery.accumulate_preprocessed_record.

accumulate_record

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accumulate_record(
    params, sample_state, record
)

Accumulates a single record into the sample state.

This is a helper method that simply delegates to preprocess_record and accumulate_preprocessed_record for the common case when both of those functions run on a single device. Typically this will be a simple sum.

Args
params The parameters for the sample. In standard DP-SGD training, the clipping norm for the sample's microbatch gradients (i.e., a maximum norm magnitude to which each gradient is clipped)
sample_state The current sample state. In standard DP-SGD training, the accumulated sum of previous clipped microbatch gradients.
record The record to accumulate. In standard DP-SGD training, the gradient computed for the examples in one microbatch, which may be the gradient for just one example (for size 1 microbatches).

Returns
The updated sample state. In standard DP-SGD training, the set of previous microbatch gradients with the addition of the record argument.

derive_metrics

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derive_metrics(
    global_state
)

Implements tensorflow_privacy.DPQuery.derive_metrics.

derive_sample_params

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derive_sample_params(
    global_state
)

Implements tensorflow_privacy.DPQuery.derive_sample_params.

get_noised_result

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get_noised_result(
    sample_state, global_state
)

Implements tensorflow_privacy.DPQuery.get_noised_result.

initial_global_state

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initial_global_state()

Implements tensorflow_privacy.DPQuery.initial_global_state.

initial_sample_state

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initial_sample_state(
    template=None
)

Implements tensorflow_privacy.DPQuery.initial_sample_state.

merge_sample_states

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merge_sample_states(
    sample_state_1, sample_state_2
)

Implements tensorflow_privacy.DPQuery.merge_sample_states.

preprocess_record

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preprocess_record(
    params, record
)

Implements tensorflow_privacy.DPQuery.preprocess_record.