Towards Generalized and Distributed Privacy-Preserving Representation Learning

10/05/2020
by   Sheikh Shams Azam, et al.
0

We study the problem of learning data representations that are private yet informative, i.e., providing information about intended "ally" targets while obfuscating sensitive "adversary" attributes. We propose a novel framework, Exclusion-Inclusion Generative Adversarial Network (EIGAN), that generalizes existing adversarial privacy-preserving representation learning (PPRL) approaches to generate data encodings that account for multiple possibly overlapping ally and adversary targets. Preserving privacy is even more difficult when the data is collected across multiple distributed nodes, which for privacy reasons may not wish to share their data even for PPRL training. Thus, learning such data representations at each node in a distributed manner (i.e., without transmitting source data) is of particular importance. This motivates us to develop D-EIGAN, the first distributed PPRL method, based on federated learning with fractional parameter sharing to account for communication resource limitations. We theoretically analyze the behavior of adversaries under the optimal EIGAN and D-EIGAN encoders and consider the impact of dependencies among ally and adversary tasks on the encoder performance. Our experiments on real-world and synthetic datasets demonstrate the advantages of EIGAN encodings in terms of accuracy, robustness, and scalability; in particular, we show that EIGAN outperforms the previous state-of-the-art by a significant accuracy margin (47 experiments further reveal that D-EIGAN's performance is consistent with EIGAN under different node data distributions and is resilient to communication constraints.

READ FULL TEXT

page 7

page 17

page 18

page 19

research
04/29/2021

Privacy-Preserving Federated Learning on Partitioned Attributes

Real-world data is usually segmented by attributes and distributed acros...
research
12/13/2020

Privacy-preserving Decentralized Aggregation for Federated Learning

Federated learning is a promising framework for learning over decentrali...
research
08/30/2020

Adversarial Privacy Preserving Graph Embedding against Inference Attack

Recently, the surge in popularity of Internet of Things (IoT), mobile de...
research
04/28/2015

Private Disclosure of Information in Health Tele-monitoring

We present a novel framework, called Private Disclosure of Information (...
research
11/13/2019

Asynchronous Distributed Learning from Constraints

In this paper, the extension of the framework of Learning from Constrain...
research
06/14/2020

Adversarial representation learning for synthetic replacement of private attributes

The collection of large datasets allows for advanced analytics that can ...
research
10/04/2018

Finding Solutions to Generative Adversarial Privacy

We present heuristics for solving the maximin problem induced by the gen...

Please sign up or login with your details

Forgot password? Click here to reset