https://github.com/tf-encrypted/tf-pjc
Bridge between TensorFlow and Google's Private Join and Compute library
https://github.com/tf-encrypted/tf-pjc
cryptography homomorphic-encryption machine-learning private-intersection-sum private-set-intersection secure-computation tensorflow
Last synced: about 1 month ago
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Bridge between TensorFlow and Google's Private Join and Compute library
- Host: GitHub
- URL: https://github.com/tf-encrypted/tf-pjc
- Owner: tf-encrypted
- License: apache-2.0
- Created: 2019-08-08T16:07:35.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2022-12-27T15:37:08.000Z (over 2 years ago)
- Last Synced: 2025-03-24T04:08:04.296Z (about 2 months ago)
- Topics: cryptography, homomorphic-encryption, machine-learning, private-intersection-sum, private-set-intersection, secure-computation, tensorflow
- Language: Python
- Homepage:
- Size: 32.2 KB
- Stars: 16
- Watchers: 3
- Forks: 3
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# TF PJC
TF PJC provides a bridge between TensorFlow and Google's [Private Join and Compute](https://github.com/google/private-join-and-compute) library. This allows two parties to privately compute the intersection of two sets and the sum of associated values as described in [IKNP+'19](https://eprint.iacr.org/2019/723).
## Usage
The library may be used as shown in the following example:
```python
import tensorflow as tf
import tf_pjc
# device strings of the two players involved
client_device = "/job:localhost/task:0/device:CPU:0"
server_device = "/job:localhost/task:1/device:CPU:0"
# construct private input of server
with tf.device(server_device):
server_elements = tf.constant(["a", "b", "c"])
# construct private inputs of client
with tf.device(client_device):
client_elements = tf.constant(["a", "b", "c", "d"])
client_values = tf.constant([100, 200, 400, 800])
# use protocol to securely compute intersection size and sum
protocol_instance = tf_pjc.PrivateIntersectionSum(client_device, server_device)
client_result_op, server_wait_op = protocol_instance(client_elements, client_values, server_elements)
# print private result (which is local to the client)
with tf.device(client_device):
intersection_size, intersection_sum = client_result_op
print_size_op = tf.print("Intersection size: ", intersection_size)
print_sum_op = tf.print("Intersection sum: ", intersection_sum)
print_op = tf.group(print_size_op, print_sum_op)
# run in TensorFlow session
with tf.Session() as sess:
sess.run([print_op, server_wait_op])
```
Future releases will also include the possibility of using TF PJC in conjunction with [TF Encrypted](https://github.com/tf-encrypted/tf-encrypted) as a kernel for `tfe.sets.intersection_sum`.
## Installation
Python 3 packages are available from [PyPI](https://pypi.org/project/tf-pjc/):
```
pip install tf-pjc
```