Create Linf contribution bounder analysis #532
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dvadym
changed the title
RamSaw
approved these changes
Jan 13, 2025
analysis/contribution_bounders.py
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| def __init__(self, partitions_sampling_prob: float): | ||
| def __init__(self, | ||
| partitions_sampling_prob: float, | ||
| perform_cross_partition_contribution_bounding: bool = True): |
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why do we need this param if we have LinfAnalysisContributionBounder? If it is false then it is the smae as LinfAnalysisContributionBounder.
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Thanks, this is leftover of 1st approach that I though (with 1 class, instead of 2)
| bound_result = self._run_contribution_bounding(input, | ||
| aggregate_fn=lambda x: x) | ||
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| expected_result = [(('pid1', 'pk2'), (2, 7, 1, 2)), |
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Why (2, 7, 1, 2) if pk2 has only 3 and 4 values contributed?
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# the output format:
# (count_per_partition, sum_per_partition,
# num_partition_contributed_per_privacy_id,
# num_contribution_per_privacy_id)
# Since no cross-partition contribution, we consider that the privacy id
# contributes only to this partition, so
# num_partition_contributed_per_privacy_id = 1
# num_contribution_per_privacy_id = count_per_partition
dvadym
commented
Jan 14, 2025
analysis/contribution_bounders.py
Outdated
| def __init__(self, partitions_sampling_prob: float): | ||
| def __init__(self, | ||
| partitions_sampling_prob: float, | ||
| perform_cross_partition_contribution_bounding: bool = True): |
There was a problem hiding this comment.
Thanks, this is leftover of 1st approach that I though (with 1 class, instead of 2)
| bound_result = self._run_contribution_bounding(input, | ||
| aggregate_fn=lambda x: x) | ||
|
|
||
| expected_result = [(('pid1', 'pk2'), (2, 7, 1, 2)), |
There was a problem hiding this comment.
Added comment
# the output format:
# (count_per_partition, sum_per_partition,
# num_partition_contributed_per_privacy_id,
# num_contribution_per_privacy_id)
# Since no cross-partition contribution, we consider that the privacy id
# contributes only to this partition, so
# num_partition_contributed_per_privacy_id = 1
# num_contribution_per_privacy_id = count_per_partition
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This is used for analysis w/o cross partition contribution bounding