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Abstract Voice-activated commands have become a key feature of popular devices such as smartphones, home assistants, and wearables. For convenience, many people configure their devices to be ‘always on’ and listening for voice commands from the user using a trigger phrase such as “Hey Siri,” “Okay Google,” or “Alexa.” However, false positives for these triggers often result in privacy violations with conversations being inadvertently uploaded to the cloud. In addition, malware that can record one’s conversations remains a signifi-cant threat to privacy. Unlike with cameras, which people can physically obscure and be assured of their privacy, people do not have a way of knowing whether their microphone is indeed off and are left with no tangible defenses against voice based attacks. We envision a general-purpose physical defense that uses a speaker to inject specialized obfuscating ‘babble noise’ into the microphones of devices to protect against automated and human based attacks. We present a comprehensive study of how specially crafted, personalized ‘babble’ noise (‘MyBabble’) can be effective at moderate signal-to-noise ratios and can provide a viable defense against microphone based eavesdropping attacks. 

Camera based assistive technologies such as smart glasses can provide people with visual impairments (PVIs) information about people in their vicinity. Although such ‘visually available’ information can enhance one’s social interactions, the privacy implications for bystanders from the perspective of PVIs remains underexplored. Motivated by prior findings of bystanders’ perspectives, we conducted two online surveys with visually impaired (N=128) and sighted (N=136) participants with two ‘field-of-view’ (FoV) experimental conditions related to whether information about bystanders was gathered from the front of the glasses or all directions. We found that PVIs considered it as ‘fair’ and equally useful to receive information from all directions. However, they reported being uncomfortable in receiving some visually apparent information (such as weight and gender) about bystanders as they felt it was ‘impolite’ or ‘improper’. Both PVIs and bystanders shared concerns about the fallibility of AI, where bystanders can be misrepresented by the devices. Our finding suggests that beyond issues of social stigma, both PVIs and bystanders have shared concerns that need to be considered to improve the social acceptability of camera based assistive technologies.