# Conservative Interference Injection to Minimize Wi-Fi Sensing Privacy Risks and Bandwidth Loss
## 8 Concluding Remarks  
This paper introduces a conservative interference injection strategy to counteract privacy and security threats from Wi-Fi sensing. Through a survey of prior work in Wi-Fi sensing as well as the construction of our own testbed, we demonstrate that fine-grained human activities are under threat of being monitored with high accuracy. By experimental analysis using commodity devices, we demonstrate that this high accuracy of Wi-Fi sensing is highly dependent on the CSI data rate. We subsequently showed analytically and experimentally that this CSI data rate can be intentionally disrupted by other devices in the wireless channel. This principle is the basis of prior work in the state-of-the-art that utilises wireless interference to reduce the CSI rate and diminish sensing accuracy, thereby protecting user privacy. This paper further investigates this, revealing a positive correlation between the degree of interference introduced and the degradation in sensing  
Figure 21: Privacy gains vs. BW.  
Figure 20: BW vs. interference.  
accuracy. This establishes a trade-off between achieving increased privacy and maintaining acceptable channel throughput for other Wi-Fi users. Compared to state-of-the-art approaches, we can thwart sensing applications with comparable efficacy at a fraction of the cost to channel bandwidth by injecting interference conservatively. Although our approach is applied specifically to keystroke recognition, the results are generalizable to other Wi-Fi-based gesture recognition systems, where interference can degrade the resolution of environmental movement. We suggest that future work should investigate the suitability of this conservative interference injection scheme in commercial settings under interference from additional devices. In such environments, the consumption of channel resources by other devices will create uncertainty around our ability to finely control the CSI rate of a sensing system. Furthermore, the increased environmental noise and poor Signal-to-Noise Ratio (SNR) of our interference devices may impede our ability to generate strong contention in the channel. Finally, whilst the methods and results in this paper are generalisable across Wi-Fi sensing applications, we motivate future work to apply it against coarse-grained sensing applications such as movement recognition. Future work should investigate whether these coarse-grained sensing applications, with relatively simple ML decision algorithms, are as sensitive to the CSI data rate and adversarial interference.  
Conceptualization, All; methodology, All; software, A.S. ([PERSON]) and H.W.; validation, A.S. ([PERSON]) and D.M.; formal analysis, All; investigation, All; resources, All; data curation, A.S. ([PERSON]) and H.W.; writing--original draft preparation, A.S. ([PERSON]) and H.W. and D.M.; writing--review and editing, D.M. and A.S. ([PERSON]); visualization, All; supervision, D.M. and A.S. ([PERSON]); project administration, D.M. and A.S. ([PERSON]); funding acquisition, D.M. and A.S. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was supported in parts by the Australian Research Council's Discovery Early Career Researcher Award (DECRA) under Grant number DE230101391 and by the Australian National Intelligence and Security Discovery Research Grants (NISDRG) under Grant number NI240100147. [PERSON]'s participation is supported by the Cyber Security Cooperative Research Centre Limited, whose activities are partially funded by the Australian Government's Cooperative Research Centres Programme.  
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.  
The authors declare no conflicts of interest.