8 research outputs found
Robust filtering schemes for machine learning systems to defend Adversarial Attack
Robust filtering schemes for machine learning systems to defend Adversarial Attac
Privacy Leakage in Smart Homes and Its Mitigation: IFTTT as a Case Study
The combination of smart home platforms and automation apps introduces much
convenience to smart home users. However, this also brings the potential for
privacy leakage. If a smart home platform is permitted to collect all the
events of a user day and night, then the platform will learn the behavior
patterns of this user before long. In this paper, we investigate how IFTTT, one
of the most popular smart home platforms, has the capability of monitoring the
daily life of a user in a variety of ways that are hardly noticeable. Moreover,
we propose multiple ideas for mitigating privacy leakages, which altogether
forms a Filter-and-Fuzz (F&F) process: first, it filters out events unneeded by
the IFTTT platform; then, it fuzzes the values and frequencies of the remaining
events. We evaluate the F&F process, and the results show that the proposed
solution makes IFTTT unable to recognize any of the user's behavior patterns