  Internet of Things (IoT) devices are becoming increasingly popular and are
influencing many application domains such as healthcare and transportation.
These devices are used for real-world applications such as sensor monitoring,
real-time control. In this work, we look at differentially private (DP) neural
network (NN) based network intrusion detection systems (NIDS) to detect
intrusion attacks on networks of such IoT devices. Existing NN training
solutions in this domain either ignore privacy considerations or assume that
the privacy requirements are homogeneous across all users. We show that the
performance of existing differentially private stochastic methods degrade for
clients with non-identical data distributions when clients' privacy
requirements are heterogeneous. We define a cohort-based $(\epsilon,\delta)$-DP
framework that models the more practical setting of IoT device cohorts with
non-identical clients and heterogeneous privacy requirements. We propose two
novel continual-learning based DP training methods that are designed to improve
model performance in the aforementioned setting. To the best of our knowledge,
ours is the first system that employs a continual learning-based approach to
handle heterogeneity in client privacy requirements. We evaluate our approach
on real datasets and show that our techniques outperform the baselines. We also
show that our methods are robust to hyperparameter changes. Lastly, we show
that one of our proposed methods can easily adapt to post-hoc relaxations of
client privacy requirements.
