pure-ldp 1.0.1

Simple pure LDP frequency oracle implementations

Pure-LDP

pure-LDP is a Python package that provides simple implementations of pure LDP frequency oracles detailed in the paper "Locally Differentially Private Protocols for Frequency Estimation" by Wang et al

The package has implementations of all three main techniques:

1. Unary Encoding - Under pure_ldp.local_hashing.ue_client and pure_ldp.local_hashing.ue_server
2. Histogram encoding - Under pure_ldp.local_hashing.he_client and pure_ldp.local_hashing.he_sever
3. Local Hashing - Under pure_ldp.local_hashing.lh_client and pure_ldp.local_hashing.lh_server

Installation

Use the package manager pip to install.

pip install pure-ldp

Requires numpy, scipy and xxhash.

Usage

import numpy as np
from pure-ldp.local_hashing.lh_client import LHClient
from pure-ldp.local_hashing.lh_server import LHServer

# Using Optimal Local Hashing (OLH)

epsilon = 3 # Privacy budget of 3
d = 4 # For simplicity, we use a dataset with 4 possible data items

client_olh = LHClient(epsilon=epsilon, use_olh=True)
server_olh = LHServer(epsilon=epsilon, d=d, use_olh=True)

# Test dataset, every user has a number between 1-4, 10,000 users total
data = np.concatenate(([1]*4000, [2]*3000, [3]*2000, [4]*1000))

for index, item in enumerate(data):
    # Simulate client-side process
    priv_data = client_olh.privatise(item, index) # We use the user's index as a hash seed, in practice this should be randomly+uniquely generated

    # Simulate server-side aggregation
    server_olh.aggregate(priv_data, index)

# Simulate server-side estimation
print(server_olh.estimate(1)) # Should be approximately 4000 +- 100

Checkout example.py for more examples.

TODO

1. More documentation
2. Implementation of PEM

Contributing

If you feel like this package could be improved in any way, open an issue or make a pull request!

License

MIT