-
-
Notifications
You must be signed in to change notification settings - Fork 77
/
private_spark.py
303 lines (245 loc) · 12.6 KB
/
private_spark.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
# Copyright 2022 OpenMined.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from pyspark import RDD
from typing import Callable
import pipeline_dp
from pipeline_dp import aggregate_params, budget_accounting
class PrivateRDD:
"""A Spark RDD counterpart.
PrivateRDD guarantees that only data that has been aggregated
in a DP manner, using no more than the specified privacy
budget, can be extracted from it through its API.
PrivateRDD keeps a `privacy_id` for each element
in order to guarantee correct DP computations.
"""
def __init__(self, rdd, budget_accountant, privacy_id_extractor=None):
if privacy_id_extractor:
self._rdd = rdd.map(lambda x: (privacy_id_extractor(x), x))
else:
# It's assumed that rdd is already in format (privacy_id, value)
self._rdd = rdd
self._budget_accountant = budget_accountant
def map(self, fn: Callable) -> 'PrivateRDD':
"""A Spark map equivalent.
Keeps track of privacy_id for each element.
The output PrivateRDD has the same BudgetAccountant as this one.
"""
# Assumes that `self._rdd` consists of tuples `(privacy_id, element)`
# and transforms each `element` according to the supplied function `fn`.
rdd = self._rdd.mapValues(fn)
return make_private(rdd, self._budget_accountant, None)
def flat_map(self, fn: Callable) -> 'PrivateRDD':
"""A Spark flatMap equivalent.
Keeps track of privacy_id for each element.
The output PrivateRDD has the same BudgetAccountant as this one.
"""
# Assumes that `self._rdd` consists of tuples `(privacy_id, element)`
# and transforms each `element` according to the supplied function `fn`.
rdd = self._rdd.flatMapValues(fn)
return make_private(rdd, self._budget_accountant, None)
def variance(self,
variance_params: aggregate_params.VarianceParams,
public_partitions=None) -> RDD:
"""Computes a DP variance.
Args:
variance_params: parameters for calculation
public_partitions: A collection of partition keys that will be present in
the result. Optional. If not provided, partitions will be selected in a DP
manner.
"""
backend = pipeline_dp.SparkRDDBackend(self._rdd.context)
dp_engine = pipeline_dp.DPEngine(self._budget_accountant, backend)
params = pipeline_dp.AggregateParams(
noise_kind=variance_params.noise_kind,
metrics=[pipeline_dp.Metrics.VARIANCE],
max_partitions_contributed=variance_params.
max_partitions_contributed,
max_contributions_per_partition=variance_params.
max_contributions_per_partition,
min_value=variance_params.min_value,
max_value=variance_params.max_value,
budget_weight=variance_params.budget_weight)
data_extractors = pipeline_dp.DataExtractors(
partition_extractor=lambda x: variance_params.partition_extractor(x[
1]),
privacy_id_extractor=lambda x: x[0],
value_extractor=lambda x: variance_params.value_extractor(x[1]))
dp_result = dp_engine.aggregate(self._rdd, params, data_extractors,
public_partitions)
# dp_result : (partition_key, (variance=dp_variance))
# aggregate() returns a namedtuple of metrics for each partition key.
# Here is only one metric - variance. Extract it from the list.
dp_result = backend.map_values(dp_result, lambda v: v.variance,
"Extract variance")
# dp_result : (partition_key, dp_variance)
return dp_result
def mean(self,
mean_params: aggregate_params.MeanParams,
public_partitions=None) -> RDD:
"""Computes a DP mean.
Args:
mean_params: parameters for calculation
public_partitions: A collection of partition keys that will be present in
the result. Optional. If not provided, partitions will be selected in a DP
manner.
"""
backend = pipeline_dp.SparkRDDBackend(self._rdd.context)
dp_engine = pipeline_dp.DPEngine(self._budget_accountant, backend)
params = pipeline_dp.AggregateParams(
noise_kind=mean_params.noise_kind,
metrics=[pipeline_dp.Metrics.MEAN],
max_partitions_contributed=mean_params.max_partitions_contributed,
max_contributions_per_partition=mean_params.
max_contributions_per_partition,
min_value=mean_params.min_value,
max_value=mean_params.max_value,
budget_weight=mean_params.budget_weight)
data_extractors = pipeline_dp.DataExtractors(
partition_extractor=lambda x: mean_params.partition_extractor(x[1]),
privacy_id_extractor=lambda x: x[0],
value_extractor=lambda x: mean_params.value_extractor(x[1]))
dp_result = dp_engine.aggregate(self._rdd, params, data_extractors,
public_partitions)
# dp_result : (partition_key, (mean=dp_mean))
# aggregate() returns a namedtuple of metrics for each partition key.
# Here is only one metric - mean. Extract it from the list.
dp_result = backend.map_values(dp_result, lambda v: v.mean,
"Extract mean")
# dp_result : (partition_key, dp_mean)
return dp_result
def sum(self,
sum_params: aggregate_params.SumParams,
public_partitions=None) -> RDD:
"""Computes a DP sum.
Args:
sum_params: parameters for calculation
public_partitions: A collection of partition keys that will be present in
the result. Optional. If not provided, partitions will be selected in a DP
manner.
"""
backend = pipeline_dp.SparkRDDBackend(self._rdd.context)
dp_engine = pipeline_dp.DPEngine(self._budget_accountant, backend)
params = pipeline_dp.AggregateParams(
noise_kind=sum_params.noise_kind,
metrics=[pipeline_dp.Metrics.SUM],
max_partitions_contributed=sum_params.max_partitions_contributed,
max_contributions_per_partition=sum_params.
max_contributions_per_partition,
min_value=sum_params.min_value,
max_value=sum_params.max_value,
budget_weight=sum_params.budget_weight)
data_extractors = pipeline_dp.DataExtractors(
partition_extractor=lambda x: sum_params.partition_extractor(x[1]),
privacy_id_extractor=lambda x: x[0],
value_extractor=lambda x: sum_params.value_extractor(x[1]))
dp_result = dp_engine.aggregate(self._rdd, params, data_extractors,
public_partitions)
# dp_result : (partition_key, (sum=dp_sum))
# aggregate() returns a namedtuple of metrics for each partition key.
# Here is only one metric - sum. Extract it from the list.
dp_result = backend.map_values(dp_result, lambda v: v.sum,
"Extract sum")
# dp_result : (partition_key, dp_sum)
return dp_result
def count(self,
count_params: aggregate_params.CountParams,
public_partitions=None) -> RDD:
"""Computes a DP count.
Args:
count_params: parameters for calculation
public_partitions: A collection of partition keys that will be present in
the result. Optional. If not provided, partitions will be selected in a DP
manner.
"""
backend = pipeline_dp.SparkRDDBackend(self._rdd.context)
dp_engine = pipeline_dp.DPEngine(self._budget_accountant, backend)
params = pipeline_dp.AggregateParams(
noise_kind=count_params.noise_kind,
metrics=[pipeline_dp.Metrics.COUNT],
max_partitions_contributed=count_params.max_partitions_contributed,
max_contributions_per_partition=count_params.
max_contributions_per_partition,
budget_weight=count_params.budget_weight)
data_extractors = pipeline_dp.DataExtractors(
partition_extractor=lambda x: count_params.partition_extractor(x[1]
),
privacy_id_extractor=lambda x: x[0],
value_extractor=lambda x: None)
dp_result = dp_engine.aggregate(self._rdd, params, data_extractors,
public_partitions)
# dp_result : (partition_key, (count=dp_count))
# aggregate() returns a namedtuple of metrics for each partition key.
# Here is only one metric - count. Extract it from the list.
dp_result = backend.map_values(dp_result, lambda v: v.count,
"Extract count")
# dp_result : (partition_key, dp_count)
return dp_result
def privacy_id_count(
self,
privacy_id_count_params: aggregate_params.PrivacyIdCountParams,
public_partitions=None) -> RDD:
"""Computes a DP Privacy ID count.
Args:
privacy_id_count_params: parameters for calculation
public_partitions: A collection of partition keys that will be present in
the result. Optional. If not provided, partitions will be selected in a DP
manner.
"""
backend = pipeline_dp.SparkRDDBackend(self._rdd.context)
dp_engine = pipeline_dp.DPEngine(self._budget_accountant, backend)
params = pipeline_dp.AggregateParams(
noise_kind=privacy_id_count_params.noise_kind,
metrics=[pipeline_dp.Metrics.PRIVACY_ID_COUNT],
max_partitions_contributed=privacy_id_count_params.
max_partitions_contributed,
max_contributions_per_partition=1)
data_extractors = pipeline_dp.DataExtractors(
partition_extractor=lambda x: privacy_id_count_params.
partition_extractor(x[1]),
privacy_id_extractor=lambda x: x[0],
# PrivacyIdCount ignores values.
value_extractor=lambda x: None)
dp_result = dp_engine.aggregate(self._rdd, params, data_extractors,
public_partitions)
# dp_result : (partition_key, (privacy_id_count=dp_privacy_id_count))
# aggregate() returns a namedtuple of metrics for each partition key.
# Here is only one metric - privacy id count. Extract it from the list.
dp_result = backend.map_values(dp_result, lambda v: v.privacy_id_count,
"Extract privacy id count")
# dp_result : (partition_key, dp_privacy_id_count)
return dp_result
def select_partitions(
self,
select_partitions_params: aggregate_params.SelectPartitionsParams,
partition_extractor: Callable) -> RDD:
"""Computes a collection of partition keys in a DP manner.
Args:
select_partitions_params: parameters for calculation
partition_extractor: function for extracting partition key from each input element
"""
backend = pipeline_dp.SparkRDDBackend(self._rdd.context)
dp_engine = pipeline_dp.DPEngine(self._budget_accountant, backend)
params = pipeline_dp.SelectPartitionsParams(
max_partitions_contributed=select_partitions_params.
max_partitions_contributed)
data_extractors = pipeline_dp.DataExtractors(
partition_extractor=lambda x: partition_extractor(x[1]),
privacy_id_extractor=lambda x: x[0])
return dp_engine.select_partitions(self._rdd, params, data_extractors)
def make_private(rdd: RDD,
budget_accountant: budget_accounting.BudgetAccountant,
privacy_id_extractor: Callable) -> PrivateRDD:
"""A factory method for creating PrivateRDDs."""
prdd = PrivateRDD(rdd, budget_accountant, privacy_id_extractor)
return prdd