1 research outputs found
Differentially Private Deep Learning with Smooth Sensitivity
Ensuring the privacy of sensitive data used to train modern machine learning
models is of paramount importance in many areas of practice. One approach to
study these concerns is through the lens of differential privacy. In this
framework, privacy guarantees are generally obtained by perturbing models in
such a way that specifics of data used to train the model are made ambiguous. A
particular instance of this approach is through a "teacher-student" framework,
wherein the teacher, who owns the sensitive data, provides the student with
useful, but noisy, information, hopefully allowing the student model to perform
well on a given task without access to particular features of the sensitive
data. Because stronger privacy guarantees generally involve more significant
perturbation on the part of the teacher, deploying existing frameworks
fundamentally involves a trade-off between student's performance and privacy
guarantee. One of the most important techniques used in previous works involves
an ensemble of teacher models, which return information to a student based on a
noisy voting procedure. In this work, we propose a novel voting mechanism with
smooth sensitivity, which we call Immutable Noisy ArgMax, that, under certain
conditions, can bear very large random noising from the teacher without
affecting the useful information transferred to the student.
Compared with previous work, our approach improves over the state-of-the-art
methods on all measures, and scale to larger tasks with both better performance
and stronger privacy (). This new proposed framework can be
applied with any machine learning models, and provides an appealing solution
for tasks that requires training on a large amount of data