  The success of deep learning partially benefits from the availability of
various large-scale datasets. These datasets are often crowdsourced from
individual users and contain private information like gender, age, etc. The
emerging privacy concerns from users on data sharing hinder the generation or
use of crowdsourcing datasets and lead to hunger of training data for new deep
learning applications. One na\"{\i}ve solution is to pre-process the raw data
to extract features at the user-side, and then only the extracted features will
be sent to the data collector. Unfortunately, attackers can still exploit these
extracted features to train an adversary classifier to infer private
attributes. Some prior arts leveraged game theory to protect private
attributes. However, these defenses are designed for known primary learning
tasks, the extracted features work poorly for unknown learning tasks. To tackle
the case where the learning task may be unknown or changing, we present TIPRDC,
a task-independent privacy-respecting data crowdsourcing framework with
anonymized intermediate representation. The goal of this framework is to learn
a feature extractor that can hide the privacy information from the intermediate
representations; while maximally retaining the original information embedded in
the raw data for the data collector to accomplish unknown learning tasks. We
design a hybrid training method to learn the anonymized intermediate
representation: (1) an adversarial training process for hiding private
information from features; (2) maximally retain original information using a
neural-network-based mutual information estimator.

Machine Learning;Cryptography and Security;Machine Learning