tf_privacy.NoPrivacySumQuery

Implements DPQuery interface for a sum query with no privacy.

Inherits From: SumAggregationDPQuery, DPQuery

Accumulates vectors without clipping or adding noise.

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
)

Derives metric information from the current global state.

Any metrics returned should be derived only from privatized quantities.

Args
global_state	The global state from which to derive metrics.

Returns
A collections.OrderedDict mapping string metric names to tensor values.

derive_sample_params

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derive_sample_params(
    global_state
)

Given the global state, derives parameters to use for the next sample.

For example, if the mechanism needs to clip records to bound the norm, the clipping norm should be part of the sample params. In a distributed context, this is the part of the state that would be sent to the workers so they can process records.

Args
global_state	The current global state.

Returns
Parameters to use to process records in the next sample.

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()

Returns the initial global state for the DPQuery.

The global state contains any state information that changes across repeated applications of the mechanism. The default implementation returns just an empty tuple for implementing classes that do not have any persistent state.

This object must be processable via tf.nest.map_structure.

Returns
The 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
)

Preprocesses a single record.

This preprocessing is applied to one client's record, e.g. selecting vectors and clipping them to a fixed L2 norm. This method can be executed in a separate TF session, or even on a different machine, so it should not depend on any TF inputs other than those provided as input arguments. In particular, implementations should avoid accessing any TF tensors or variables that are stored in self.

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)
record	The record to be processed. 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
A structure of tensors to be aggregated.