Although local differential privacy (LDP) protects individual users’ data
from inference by an untrusted data curator, recent studies show that an
attacker can launch a data poisoning attack from the user side to inject
carefully-crafted bogus data into the LDP protocols in order to maximally skew
the final estimate by the data curator.

In this work, we further advance this knowledge by proposing a new
fine-grained attack, which allows the attacker to fine-tune and simultaneously
manipulate mean and variance estimations that are popular analytical tasks for
many real-world applications. To accomplish this goal, the attack leverages the
characteristics of LDP to inject fake data into the output domain of the local
LDP instance. We call our attack the output poisoning attack (OPA). We observe
a security-privacy consistency where a small privacy loss enhances the security
of LDP, which contradicts the known security-privacy trade-off from prior work.
We further study the consistency and reveal a more holistic view of the threat
landscape of data poisoning attacks on LDP. We comprehensively evaluate our
attack against a baseline attack that intuitively provides false input to LDP.
The experimental results show that OPA outperforms the baseline on three
real-world datasets. We also propose a novel defense method that can recover
the result accuracy from polluted data collection and offer insight into the
secure LDP design.

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Author Of this post: <a href="">Xiaoguang Li</a>, <a href="">Ninghui Li</a>, <a href="">Wenhai Sun</a>, <a href="">Neil Zhenqiang Gong</a>, <a href="">Hui Li</a>

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