In last post we covered, Privacy Preserving Measurement (PPM) and covered how Distributed Aggregation Protocol (DAP) works. Today, we'll explore how to implement a simplified version of the using Python with Prio3 as our Verifiable Distributed Aggregation Function (VDAF). This implementation will support multiple clients, demonstrating how DAP can aggregate data from multiple sources while maintaining privacy. Let's dive in!
Implementing the Finite Field
First we will sets up a simple Finite Field (FF), which is crucial for the cryptographic operations in Prio3. A finite field is a set that provides a way to perform arithmetic operations with guaranteed properties that are useful for cryptographic protocols. In Prio3, finite fields are used for secret sharing and for the underlying zero-knowledge proofs. Prio3 is suitable for a wide variety of aggregation functions, including sum, mean, standard deviation, estimation of quantiles, and linear regression.
Here we setup addition, multiplication, subtraction, and division operations on top of Finite Field. Initialisation requires a prime number p. We're using largest 32-bit prime (4294967291), larger the prime better the security.
import random
class FiniteField:
def __init__(self, p):
if not self._is_prime(p):
raise ValueError("p must be prime")
self.p = p
def add(self, a, b):
return (a + b) % self.p
def sub(self, a, b):
return (a - b) % self.p
def mul(self, a, b):
return (a * b) % self.p
def div(self, a, b):
if b == 0:
raise ZeroDivisionError("Cannot divide by zero in the field")
# Use Fermat's little theorem to compute the multiplicative inverse
return self.mul(a, pow(b, self.p - 2, self.p))
def _is_prime(self, n):
if n < 2:
return False
for i in range(2, int(n ** 0.5) + 1):
if n % i == 0:
return False
return True
# Use a larger prime for better security
# This is the largest 32-bit prime
FIELD = FiniteField(4294967291)
# Example usage:
a = random.randint(0, FIELD.p - 1)
b = random.randint(0, FIELD.p - 1)
print(f"a = {a}, b = {b}")
print(f"a + b = {FIELD.add(a, b)}")
print(f"a - b = {FIELD.sub(a, b)}")
print(f"a * b = {FIELD.mul(a, b)}")
print(f"a / b = {FIELD.div(a, b)}")
Implementing Prio3
This is a simplified implementation of Prio3 which requires a FF for initialisation. We define 4 methods: shard, prepare, aggregate, and unshard.
The shard method splits a measurement into two shares, prepare is simplified to an identity function, aggregate sums the shares, and unshard combines the final aggregate shares.
class Prio3:
def __init__(self, field):
self.field = field
def shard(self, measurement, nonce):
# Ensure the measurement is in the field
measurement = measurement % self.field.p
# Generate a random share
share1 = random.randint(0, self.field.p - 1)
# Compute the second share such that share1 + share2 = measurement (mod p)
share2 = self.field.sub(measurement, share1)
return None, [share1, share2]
def prepare(self, agg_id, agg_param, nonce, public_share, input_share):
# In this simple version, preparation is just the identity function
return input_share
def aggregate(self, agg_param, output_shares):
# Sum up the output shares in the field
return sum(output_shares) % self.field.p
def unshard(self, agg_param, agg_shares, num_measurements):
# Sum up the aggregate shares in the field
return sum(agg_shares) % self.field.p
vdaf = Prio3(FIELD)
# Example usage:
measurement = 12345
nonce = os.urandom(16)
public_share, input_shares = vdaf.shard(measurement, nonce)
print(f"Original measurement: {measurement}")
print(f"Shares: {input_shares[0]}, {input_shares[1]}")
print(f"Sum of shares: {(input_shares[0] + input_shares[1]) % FIELD.p}")Defining DAP Structures
These classes define the main data structures used in DAP: reports, aggregation jobs, and collections.
class Report:
def __init__(self, report_id, time, public_share, encrypted_input_shares):
self.report_id = report_id
self.time = time
self.public_share = public_share
self.encrypted_input_shares = encrypted_input_shares
class AggregationJob:
def __init__(self, job_id, agg_param, reports):
self.job_id = job_id
self.agg_param = agg_param
self.reports = reports
class Collection:
def __init__(self, report_count, interval, encrypted_agg_shares):
self.report_count = report_count
self.interval = interval
self.encrypted_agg_shares = encrypted_agg_sharesImplementing the Client
The Client generates reports by sharding measurements and simulates uploading them to the Leader.
class Client:
def __init__(self, leader_url, helper_url, task_id):
self.leader_url = leader_url
self.helper_url = helper_url
self.task_id = task_id
def generate_report(self, measurement):
report_id = os.urandom(16)
timestamp = int(time.time())
public_share, input_shares = vdaf.shard(measurement, report_id)
encrypted_shares = input_shares
return Report(report_id, timestamp, public_share, encrypted_shares)
def upload_report(self, report):
print(f"Uploading report {report.report_id.hex()} to Leader")
return TrueImplementing the Leader
The Leader receives reports from clients, starts aggregation jobs, and processes its share of the reports.
class Leader:
def __init__(self, helper_url, collector_public_key):
self.helper_url = helper_url
self.collector_public_key = collector_public_key
self.reports = {}
self.aggregation_jobs = {}
def receive_report(self, report):
self.reports[report.report_id] = report
return True
def start_aggregation_job(self, job_id, agg_param, report_ids):
job = AggregationJob(job_id, agg_param, [self.reports[rid] for rid in report_ids])
self.aggregation_jobs[job_id] = job
output_shares = []
for report in job.reports:
input_share = report.encrypted_input_shares[0] # Leader's share
output_share = vdaf.prepare(0, agg_param, report.report_id, report.public_share, input_share)
output_shares.append(output_share)
agg_share = vdaf.aggregate(agg_param, output_shares)
print(f"The aggregate share of leader is: {agg_share}")
return agg_shareImplementing the Helper
The Helper processes its share of the reports in each aggregation job.
class Helper:
def __init__(self, collector_public_key):
self.collector_public_key = collector_public_key
def process_aggregation_job(self, job):
output_shares = []
for report in job.reports:
input_share = report.encrypted_input_shares[1] # Helper's share
output_share = vdaf.prepare(1, job.agg_param, report.report_id, report.public_share, input_share)
output_shares.append(output_share)
agg_share = vdaf.aggregate(job.agg_param, output_shares)
print(f"The aggregate share of helper is: {agg_share}")
return agg_shareImplementing the Collector
The Collector requests and processes the final aggregate result.
class Collector:
def __init__(self, leader_url, private_key):
self.leader_url = leader_url
self.private_key = private_key
def request_collection(self, task_id, batch_interval, leader_agg_share, helper_agg_share):
print(f"Requesting collection for task {task_id}")
encrypted_agg_shares = [leader_agg_share, helper_agg_share]
collection = Collection(len(leader.reports), batch_interval, encrypted_agg_shares)
return self._process_collection(collection)
def _process_collection(self, collection):
decrypted_shares = collection.encrypted_agg_shares
result = vdaf.unshard(None, decrypted_shares, collection.report_count)
return resultRunning the DAP End-to-end
Finally, we will sets up the DAP participants, creates multiple clients, generates and processes reports, and computes the final aggregate result.
# Set up the DAP participants
leader = Leader("http://helper.example", "collector_public_key")
helper = Helper("collector_public_key")
collector = Collector("http://leader.example", "collector_private_key")
# Create multiple clients and generate reports
num_clients = int(input("Enter the number of clients: "))
clients = [Client("http://leader.example", "http://helper.example", "task123") for _ in range(num_clients)]
for i, client in enumerate(clients):
measurement = int(input(f"Enter measurement for client {i+1}: "))
report = client.generate_report(measurement)
client.upload_report(report)
leader.receive_report(report)
# Leader starts an aggregation job
job_id = os.urandom(16)
agg_param = None # Not used in our simple Prio3 implementation
leader_agg_share = leader.start_aggregation_job(job_id, agg_param, [report.report_id for report in leader.reports.values()])
# Helper processes the aggregation job
helper_agg_share = helper.process_aggregation_job(leader.aggregation_jobs[job_id])
# Collector requests and processes the collection
batch_interval = (int(time.time()), 3600) # Last hour
result = collector.request_collection("task123", batch_interval, leader_agg_share, helper_agg_share)
print(f"The aggregate result (sum of all measurements) is: {result}")
print(f"The average of the measurements is: {result / num_clients}")Here is example output from the implementation,
Enter the number of clients: 3
Enter measurement for client 1: 19
Uploading report 8dcc5a57ed3f06a29c25ef02194d01ce to Leader
Enter measurement for client 2: 11
Uploading report 09249da048604887b50d0d95022847d4 to Leader
Enter measurement for client 3: 15
Uploading report 0f7f4bc8fd3f9724e7559d9184c4c869 to Leader
The aggregate share of leader is: 2424286919
The aggregate share of helper is: 1870680417
Requesting collection for task task123
The aggregate result (sum of all measurements) is: 45
The average of the measurements is: 15.0Conclusion
While this implementation is greatly simplified, it demonstrates the core concepts of DAP. In a production DAP system, each step would involve complex cryptography to ensure security and privacy. The system would also include features like batch processing of reports, minimum batch sizes to enhance privacy, various query types for different aggregation needs, robust error handling and security measures, etc. For production grade, implementation please refer libprio-rs from Divvi Up, daphne from Cloudflare, prio from original creators of Prio system and finally janus - is an experimental implementation of the DAP - from Divvi Up.