tf_privacy.SumAggregationDPQuery

Base class for DPQueries that aggregate via sum.

Inherits From: DPQuery

Methods

`accumulate_preprocessed_record`

accumulate_preprocessed_record(
    sample_state, preprocessed_record
)

Implements tensorflow_privacy.DPQuery.accumulate_preprocessed_record.

`accumulate_record`

View source

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`

View source

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`

View source

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`

View source

@abc.abstractmethod
get_noised_result(
    sample_state, global_state
)

Gets the query result after all records of sample have been accumulated.

The global state can also be updated for use in the next application of the DP mechanism.

Args
`sample_state`	The sample state after all records have been accumulated. In standard DP-SGD training, the accumulated sum of clipped microbatch gradients (in the special case of microbatches of size 1, the clipped per-example gradients).
`global_state`	The global state, storing long-term privacy bookkeeping.

Returns

Returns
A tuple `(result, new_global_state, event)` where: `result` is the result of the query, `new_global_state` is the updated global state, and `event` is the `DpEvent` that occurred. In standard DP-SGD training, the result is a gradient update comprising a noised average of the clipped gradients in the sample state---with the noise and averaging performed in a manner that guarantees differential privacy.

A tuple (result, new_global_state, event) where:

result is the result of the query,
new_global_state is the updated global state, and
event is the DpEvent that occurred. In standard DP-SGD training, the result is a gradient update comprising a noised average of the clipped gradients in the sample state---with the noise and averaging performed in a manner that guarantees differential privacy.

`initial_global_state`

View source

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`

View source

initial_sample_state(
    template=None
)

Implements tensorflow_privacy.DPQuery.initial_sample_state.

`merge_sample_states`

View source

merge_sample_states(
    sample_state_1, sample_state_2
)

Implements tensorflow_privacy.DPQuery.merge_sample_states.

`preprocess_record`

View source

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.