Understanding Sensor Notifications on Mobile Devices

Zongheng Ma, Saeed Mirzamohammadi, Ardalan Amiri Sani University of California, Irvine [email protected], [email protected], [email protected]

Abstract Mobile devices, such as smartphones and tablets, use notifi- cations to inform their users of events. Some security- and privacy-related events are time-sensitive: the user must be notified immediately. In this paper, we perform a user study with 40 participants to understand the properties of such time-sensitive notifications. We specifically focus on sensor notifications that notify the users when one of the sensitive sensors, such as camera, microphone, or location is being accessed. We show that none of the notification channels available on mobile devices, i.e., LED, vibration, sound, and display, can grab the user’s attention in more than 24% of the time. Among them, vibration achieves the best success rates on average. Moreover, our results show that less intru- (a) (b) (c) sive channels, e.g., display, can achieve significantly better Figure 1: Sensor notifications in iOS and Android: results if the device’s physical context, i.e., ambient light (a) Location notification in iOS (the notification intensity, is considered. Based on our findings, we suggest strip below the status bar on top of the screen, (b) that display notification is the best option for camera while location notification in Android (the icon in the top different vibration patterns are best options for microphone left corner), (c) microphone notification by the Sam- and location sensor. sung Voice Recorder application in Android (the icon in the top left corner). CCS Concepts •Human-centered computing → User studies; Smart- to understand the properties of such notifications, i.e., time- phones; •Security and privacy → Privacy protec- sensitive notifications, through a user study. We perform tions; Social aspects of security and privacy; Usabil- the study in the context of sensor notifications but most of ity in security and privacy; the results are applicable to other forms of time-sensitive notifications as well. Computers communicating with hu- Keywords mans is one of the most fundamental aspects of computing. In this paper, we hope to understand how effectively a mo- Sensor notifications; Mobile devices; User study bile device can grab its owner’s attention using the means available to it. In an analogy to human-to-human communi- 1. INTRODUCTION cation, this is similar to one person seeking another person’s Mobile devices use notifications to inform users of events, attention in case of potential danger. e.g., phone calls. Some security- and privacy-related notifi- We focus on sensor notifications due to their increasing cations are time-sensitive: the user must be notified imme- importance. Mobile devices incorporate a set of privacy- diately. Examples are sensor notifications that inform the sensitive sensors, most importantly, camera, microphone, user of access to sensitive sensors (e.g., camera, microphone, and location (i.e., GPS or cellular network-based sensing). and location) and AMBER alerts. In this paper, we set out These sensors can capture sensitive information of the user including photos, videos, conversations, and locations. A Permission to make digital or hard copies of all or part of this work for personal or prior study has shown that most users consider this infor- classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation mation to be private and sensitive [9]. Unfortunately, there on the first page. Copyrights for components of this work owned by others than the have been several incidents where attackers have attempted author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or to access these sensors without user’s knowledge, e.g., to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. capture unauthorized video or audio of the user or to track HotMobile ’17, February 21 - 22, 2017, Sonoma, CA, USA her whereabouts [2–4]. And tools are available for attack- ers to remotely control infected device’s camera and micro- © 2017 Copyright held by the owner/author(s). Publication rights licensed to ACM. ISBN 978-1-4503-4907-9/17/02. . . $15.00 phone [1]. To address this problem, sensor notifications are DOI: http://dx.doi.org/10.1145/3032970.3032978 used, which use some notification channel, such as LED and display, to notify the user whenever one of these sensors is quite counterintuitively, existing android notifications that being used. For example, mobile operating systems, such only rely on textual content to convey their meaning are the as Android and iOS, show some form of notification on the hardest for users to recognize in a timely manner. Finally, screen when a location sensor is accessed (Figure 1 (a) and we realize that physical context has important impact on (b)). Moreover, some applications inform the user when the effectiveness of notifications. For example, we find that they record audio in the background (Figure 1 (c)). our new display notification (which turns on the display and The rationale for sensor notifications is that users can shows a strip on top of the screen with textual content in distinguish between legitimate and illegitimate accesses to it) can be very effective in light environments. Based on these sensors. For example, if the user is using a photog- our findings, we suggest to use this display notification for raphy application, s/he expects the camera to be on. But camera since it is effective in light environments, which is when the user is watching a video, s/he expects the camera where malicious access to the camera can be effective. For to be off. Therefore, a notification about the use of camera microphone and location sensor, we suggest to use different in the latter scenario is likely to indicate malicious activity. vibration patterns since vibration achieves high success rate We identify three important requirements for a sensor no- and its different patterns are easy for users to recognize. tification. First, it must be able to attract the user’s atten- tion successfully at all times and as fast as possible. This is 2. SENSOR NOTIFICATION DESIGN important as it will minimize the window of vulnerability, We designed and implemented the notifications using An- i.e., the period of time that malware can access the sensor droid services (Android version 6.0.1, CyanogenMod version without user’s knowledge. Second, it must be unambiguous. 13.0). The implementation of a sensor notification has two The user must be able to quickly understand the meaning components: (i) detecting when a sensor is being accessed, of the notification and differentiate it from other sensor no- and (ii) triggering a notification using one of the channels. tifications and other notifications in the system, e.g., phone Detecting the Sensor Access. We implement the no- calls or application updates. Third, it should not cause an- tifications for three sensors: microphone, camera, and loca- noyance to the user. This last property is indeed important tion. We detect the access to the first two in the MediaSer- because notifications are triggered whenever the sensor is vice, which is part of the stack that implements the API accessed, even if the access is legitimate, e.g., user taking a used by applications to use these two sensors. For location, photo with the camera. we use the LocationManagerService, which implements the We ask: which one of existing notification channels, i.e., API for applications to acquire location information. LED, vibration, sound, and display, best satisfy these re- With this implementation, we can reliably detect most ap- quirements? We answer this question with a user study plication’s use of these three sensors, except for two cases: performed on 40 participants1. To perform the user study, First, we cannot detect the use of microphone for cellular we first designed and implemented various types of sensor phone calls since the modem does not use the microphone notifications for camera, microphone, and location, in the through the aforementioned API. Second, we cannot detect Android operating system (§2). We then deployed our mod- the use of Google Play location API, which some apps use ified operating system on Google Nexus 5X smartphones and (instead of LocationManagerService API) to acquire loca- distributed them to the participants to use as their primary tion information. In the study, since malicious accesses to device for one week. In addition, we installed an application sensors is emulated by an application designed by us (§3.1), on the smartphone that emulates malware by using these this limitation does not have major effect on most of the re- sensors in the background a couple of times a day, which sults. It might only limit the extent to which we can assess would result in a notification to the user by the operating user’s annoyance with notifications when apps legitimately system. We then asked the participants to log the sensor use a sensor. To mitigate this, we asked them to mainly use that was illegitimately used as soon as they spotted the cor- Skype for voice calls (rather than phone calls) and use Waze responding notification. This allows us to assess the first two for navigation (rather than Google maps), both of which use requirements mentioned earlier. To assess the third require- standard audio and location API and hence are detected by ment, we asked the participants to fill out a questionnaire our implementation. right after the study. We have made the data collected in Triggering Notifications. Once we detect that a sen- the study publicly available2. sor is being accessed, we trigger the notification for it using To better understand the notifications, we decided to eval- one of the five different notification channels supported in uate the impact of the device’s physical context, i.e., ambient our prototype. The channels are as follows: blinking white light intensity and ambient noise, on the effectiveness of the LED (0.67 Hz blinking frequency with 67% duty cycle), vi- notifications. To do this, we collected these physical context bration (1 second constant vibration), sound (3 second audio measurements whenever a notification was triggered. segment), and two notifications based on the display: one We present several important findings about sensor noti- designed by us, which turns on the display (if off) and shows fications. First, on average, none of the existing channels a strip on top of the screen with a text that mentions the achieve a success rate (in capturing user’s attention) higher name of the sensor being used (similar to iOS location noti- than 24%. Second, vibration achieves the best success rates fication in Figure 1 (a)), and one using existing notification at 24%, and LED achieves the worst at 4%. Third, we find supported by Android (which shows up in the pull-down no- that sound is almost always a bad choice as it incurs sig- tification bar, and which does not turn on the display if it nificant annoyance to the user, while being outperformed by is off). Thereafter, we refer to these last two notification vibration and even display notification. Fourth, we find that, channels as display and Android notifications, respectively. 1User study approved by UC Irvine’s Office of Research, It is important to note that the LED, display, and Android Human Research Protection, under IRB HS# 2016-3100. notifications stay on as long as the sensor is being used. On 2http://www.ics.uci.edu/~ardalan/notifdata.html the other hand, vibration and sound notifications only run for a fixed period of time (i.e., 1 and 3 seconds, respec- ysis of user study data shows that participants’ phone was tively), in order not to significantly disturb the user. One on high volume (higher half of the volume range) in 52% of might wonder why we do not leave the first group of notifica- the time and the vibration was on in 94% of the time. tions (LED, display, and Android) on indefinitely until it is Second, the app provides a mechanism for the participants noticed and cleared by the user, very similar to what many to report the sensor accesses that they spot by logging it. applications’ notifications do. This is because our goal is to More specifically, we asked the participants to open the No- assess the effectiveness of time-sensitive notifications, where tifTest app and record the name of the sensor that is being it is critical for the user to be notified when the event is illegitimately used as soon as they noticed the corresponding happening. Leaving the notification on even after the event notification. Illegitimate use refers to the use of sensors in will increase the odds of user’s noticing the notification, but the background, one that the participant does not expect. not necessarily in a timely manner. Therefore, to control Note that we assume our NotifTest app to be the only this factor in the study, we clear the notification once the source of illegitimate access to sensors. We believe that this sensor is not accessed anymore. is a reasonable assumption as the operating system image on the smartphone is freshly installed; one that is not in- 3. USER STUDY fected with malware. There are, however, some applications that legitimately use the location sensor in the background In this section, we describe the user study that we con- as well (e.g., navigation apps), which will result in the par- ducted to evaluate sensor notifications. ticipants noticing some unexpected legitimate notifications, 3.1 Recruitment and Study Logistics resulting in additional reports. In the analysis of the data, we can filter out these cases as there will be no records of the We recruited 40 participants. Each participant was given triggered notifications in our logs before the reports. Note a Nexus 5X smartphone with our instrumented Android op- that this legitimate background access to the location sensor erating system and was asked to use the smartphone for makes its notification less effective in case of illegitimate ac- one whole week. 33 of participants were undergraduate stu- cesses (compared to camera and microphone) since it makes dents and 7 of them were graduate students, mostly ma- it difficult for the user to distinguish. However, addressing joring in computer science. Moreover, 14 of them were fe- this issue is out of the scope of this paper. male. Our participants hence represent a more technically- As discussed, we attempt to understand whether users advanced portion of the population. We compensated each can correctly associate a notification with the event that it participant with a $20 gift card. represents. We use two mechanisms to answer this ques- We asked the participants to use our smartphone as their tion. First, we measure the correctness of user reports of main smartphone during the study. To facilitate this, we notifications. This shows whether they can associate the helped them set up their Google accounts on the smartphone notification channel with its corresponding sensor. Second, and transfer their SIM cards from their own phones to the for about 2 to 3 times a day, we trigger fake notifications study smartphone, if possible. Due to SIM incompatibility, that use a variation of the notification mechanism in order we managed to successfully perform the SIM card transfer to assess the user’s ability to differentiate it with an authen- only for about 10 of the participants. The other 30 still used tic notification. We have designed fake notifications for all our phone as their primary one but carried their own phone of the channels. For LED, we turn on the LED with a differ- just for phone calls. We also asked the participants to regu- ent color, i.e., blue, but with the same blinking pattern. For larly use apps that use camera, microphone, and location, in vibrator, we use a different vibrating pattern, i.e., on/off vi- order to be able to provide us with feedback on the annoy- bration for 1.9 seconds at a frequency 1.4 Hz and 71% duty ance of the notifications. We installed Skype, Waze, and a cycle. For sound, we play a 2 second audio segment, which camera application on the smartphones to facilitate the use is different from the authentic sound notification (i.e., dif- of these applications for the participants. ferent tune). For display and Android notifications, we use In addition, we developed and installed an application, a different text in the notification. For display notification, called NotifTest, on the phone, which plays two roles in we also use a different color in the strip. For Android notifi- the study. First, it emulates malware by accessing the sen- cation, we use the same icon, relying on the user to read the sors illegitimately in the background. To do this, the app text. At recruit time, we explicitly instructed the partici- uses these sensors in the background roughly 5 times a day pants that they should look for the exact form of notification at random times between 10 A.M. and 9 P.M. We limited associated with the sensors, e.g., by paying attention to the the use of sensors to this time interval in order not to disturb color of LED. However, we did not tell them that our app the participants at night time. Each time, the app uses the would trigger these fake notifications in order not to affect sensor for 5 seconds. We chose this number since it is long the result of the study. enough for malware to gain useful information from the sen- Also, as mentioned before, we are interested in under- sors and also it is short enough to challenge the notification standing the effect of physical context on the effectiveness mechanism in quickly grabbing the user’s attention. At re- of the notifications. Therefore, right before and after each cruit time, we instructed the participants about the meaning sensor access, the NotifTest app records some context mea- of each notifications, e.g., blinking white LED notifications surements. More specifically, for 5 seconds before and af- means that the camera is being used. Moreover, the No- ter each access, it records the ambient noise level using the tifTest app allows the participants to check the notification Android MediaRecorder API (i.e., getMaxAmplitude()) and for each sensor, if they forget, which helps them more easily the ambient light intensity using the light sensor. We dis- remember the meaning of each notification. We also asked able the microphone’s notification when microphone is used the participants to avoid lowering the system volume and by our own system to measure the ambient noise. disabling the vibration for extended periods of time, which would otherwise render these notifications useless. Our anal- 100 5 100 100

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0 0 0 LED Vib Snd Disp Andr LED Vib Snd Disp Andr Vib Snd Disp LED Vib Snd Disp Andr Notification Channels Notification Channels Notification Channels Notification Channels (a) (b) (a) (b) Figure 2: (a) Notification channels’ success rate in Figure 3: (a) The accuracy of associating notifica- capturing the participants’ attention. (b) Partici- tion channels with sensors. (b) The success of fake pants’ delay in reporting the notifications. notifications in fooling the participants.

At the end of the study, we asked the participants fill out a Figures 2 (a) and (b) show the success rate and report questionnaire about their experience with the notifications. delay for different channels, respectively. We have the fol- Specifically, we asked them about the annoyance caused to lowing findings. them by each notification channel. Finding 1. All of the existing notification channels in mo- bile devices today achieve relatively low success rate on aver- 3.2 Notification Assignment age (less than 24%). This is a significant finding as it shows As mentioned previously, we support notifications for three that system and application designers cannot expect to be sensors, i.e., camera, microphone, and location, and five no- able to grab the user’s attention quickly and successfully at tification channels, i.e., LED, vibration, sound, display, and all times. Android notifications. Therefore, there are 15 combinations Finding 2. Vibrator achieve the highest success rate at of (sensor, notification) pairs. We deploy all 15 pairs in our 24%, whereas LED achieves the lowest at 4%. This result user study. Each user can test three of these combinations is also significant since LED is commonly used for notifica- (since there are three sensors). Therefore, all combinations tions in mobile devices these days. Our study shows that can be tested with five participants. As a result, since we LED notifications are largely ineffective for time-sensitive recruited 40 participants, every combination is tested by 8 notifications. different participants. Finding 3. There is a reverse relationship between success rate and report delay. We believe there are two reasons be- hind this. First, notifications that are more successful in 4. RESULTS grabbing the user’s attention, do so faster on average. Sec- We use the data collected in the user study to answer ond, vibration and sound achieve lower report delay since several questions about sensor notifications. We present ev- they are more intrusive and cause the user to (maybe un- ery measured metric as an average (along with maximum knowingly) take action faster. and minimum shown using error-bars in figures) over all the Q2. How unambiguous is each notification channel? participants who qualified for that metric (e.g., vibration As mentioned before, we attempt to understand whether success rate is shown as average among all users who were users can correctly associate the notification with the event assigned the vibration channel for one of the sensors). More- that it represents. We answer this question using two met- over, we only considered the participants who had at least rics. First, we measure the correctness of user reports of no- 3 data points for the metric (e.g., three vibration notifica- tifications. That is, for notifications that successfully grab tions) and made sure that at least 3 participants qualify for the user’s attention, we measure how often the user reports the metric in order to have statistically reliable results. the accessed sensors correctly. Second, we measure the per- Q1. How effective is each notification channel in attracting centage of times that the user falls for fake (i.e., variations the user’s attention? of) notifications (§3.1). We define effectiveness in terms of success rate and report Figures 3 (a) and (b) show these metrics, respectively. delay. More specifically, we measure the percentage of times Note that, in both figures, we only show the channels for that the user reported a sensor usage after a notification which we have enough data to make a statistically reliable was triggered using a specific channel. We also measure conclusion (§4). The missing results for LED and Android the delay of the report. Note that we put an upper bound notifications in Figure 3 (a) is because we do not have at of 5 minutes on the delay. That is, if the report comes least 3 users, each with at least three successful reports for after this upper bound, we do not consider it as a successful these channels. report. This is because, as mentioned in §2, notifications It is important to understand how we calculate the suc- might be triggered by existing applications, and therefore cess rate of fake notifications. One can simply report the user’s report might be as a response to those triggers. In the percentage of time that the user reports a sensor access af- recruitment, we tell the participants to report a notification ter a fake notification is triggered. However, this number as soon as possible. Therefore, we believe that this simple would underestimate the success rate of fake notifications filtering method provides good accuracy. because it is not clear whether the user did not report the 100 100 100 Dark Quiet Camera First 3 Days Light Loud Microphone Last 4 Days 80 80 Location 80 10

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0 0 0 0 LED Vib Snd Disp Andr LED Vib Snd Disp Andr LED Vib Snd Disp Andr LED Vib Snd Disp Andr Notification Channels Notification Channels Notification Channels Notification Channels (a) (b) (a) (b) Figure 4: Effects of (a) ambient light and (b) ambi- Figure 5: (a) User’s annoyance. (b) Notification’s ent noise on sensor notifications. effectiveness over time. fake notification due to missing the notification or due to that if Android notifications are to be used, it is best to use understanding that it is not the right notification. There is different icons and not merely rely on textual content. no way for us to directly measure this; therefore we use an Q3. Do notifications’ effectiveness depend on system’s phys- approximate approach. We use the success rate of the cor- ical context? responding notification channel from Figure 2 (a) to adjust In the study, we record two contextual data whenever the the results for fake notifications. In this approximation, we NotifTest triggers a notification, namely, the ambient light assume that the success rate of a notification (Figure 2) is and ambient noise. We categorize each context into two almost the same as the success rate of its variation (i.e., fake groups: light and dark, and loud and quiet. We then in- notification) in capturing the user’s attention. With this as- vestigate whether the notification’s effectiveness depend on sumption, the percentage of fake notifications that caused a these factors. We choose 5 lux as the boundary between dark report (out of all the triggered ones) divided by the success and light environments since it represents the illuminance rate of the corresponding channel gives us the percentage of at a moderately dark environment. Unfortunately, lack of fake notifications that succeed in fooling the participant out proper documentation on Android’s API did not allow us of those fake notifications that s/he notices. Due to approx- to convert the ambient noise readings to decibel. Therefore, imation, this approach can result in success rate of more for the boundary between loud and quiet, we performed ex- than 100%, in which case, we have capped the number to periments and chose one that represents a modestly quiet 100% in the Figure (the actual value of both capped bars is environment in an office environment. Figures 4 (a) and (b) about 200%). We have two important findings. show the results. We have two important findings. Finding 4. Users can correctly determine the notification Finding 6. Ambient light is an important indicator for the channel used for each sensor. This is either because they success of notifications. Most importantly, in light environ- remember the meaning of the notification or they check it ments, LED, display, and Android notifications have much if they forget (§3.1). Indeed, we found that on average par- better success rate. Indeed, the display notification sur- ticipants did check the meaning of each notification channel passes the sound in these environments. Given that speaker between 3.5 to 6.5 times in the span of one week study. has high annoyance level (see Q4 below), the system must Finding 5. Fake notifications can fool the users in many use the display notification in light environments. Vibration instances. More surprising, fake LED, display, Android no- remains the best options in dark environments, which might tifications have the highest success rate. For LED, this can represent the device being in the user’s pocket. Note that be due to difficulty of distinguishing the color (given the high ambient light might mean that the device is either be- small LED size). For display and Android notifications, this ing used by the user or sitting somewhere, e.g., on a desk. can be due to the fact that these two notification channels We do not differentiate between these cases. mainly rely on textual information to convey their meaning Finding 7. Against our intuition, even in quiet environ- (in contrast to sound and vibration that leverage different ments, vibrator achieves a better success rate than sound. patterns and audios). Given that notifications only last for 5 This finding shows that sound channel can be safely replaced seconds (while sensor is being accessed), textual content end with vibration at all times without lowering the success rate up being harder to recognize in a timely manner. Indeed, in of notification. the final questionnaire, one of the participants noted that: Q4. How much annoyance does each notification channel “notification bar/pop-up window can be hard to remember cause to the user? because sometime I’ll forget the content of notification”. We answer this question by studying the questionnaire Another interesting observation is that display notification filled out by the users. Figure 5 (a) shows the results. We is less ambiguous than the Android notification. We believe have the following findings. that this is because fake display notification uses a different Finding 8. Sound results in the highest level of annoyance color strip on the screen that can be easily detected by the to the participants followed by vibration. As mentioned, user (without the user having to read the text on it), whereas given that vibration has higher success rate than sound (even the Android notification uses the same icon, relying on the in quiet environments), this result means vibration must be user to pull-down the menu and read the text. This implies preferred over sound. Finding 9. Vibration and sound are especially intolerable immediate feedback increases the sense of privacy violation for camera. We believe this is because these notifications in users. In our study, unlike theirs, we study the effective- heavily interfere with user’s legitimate use of the camera. ness of channels for time-sensitive notifications. Q5. Do sensor notifications’ effectiveness degrade over time? Viola presented a system design for enforcing sensor noti- We measure the success rate of notifications for the first fications in a trustworthy manner [8]. It uses the hypervisor and second half of the study (i.e., first 3 days and last 4 or the operating system kernel to insert formally verified days) and show the results in in Figure 5 (b). checks in order to guarantee that a sensor notification can- Finding 10. We find that sensor notifications’ success rate not be disabled by malware. It, however, does not address degrades over time. This poses a challenge for time-sensitive the choice of the notification channel. It does not present a notifications deployed to be used for extended periods of design for implementing the notifications either. time. Note that our user study spans one week only and degradation can be worse after a longer period of time. 7. CONCLUSIONS We presented the results of a 40-participant user study 5. DISCUSSIONS conducted to understand sensor notifications on mobile de- Based on our findings, we make suggestions for the choice vices. Our study showed that none of the existing channels, of sensor notifications. First, we suggest to use our new dis- i.e., LED, vibration, sound, display, and Android notifica- play notification (§2) for the camera. This notification chan- tions achieve more than 24% success rate in grabbing the nel achieves high success rate in light environments where user’s attention. Moreover, it showed that device’s physi- malicious access to camera can invade user’s privacy. More- cal context has an important impact on the effectiveness of over, it causes low annoyance and does well in unambiguity. the notification. Based on our findings, we suggested using Second, we suggest to use different vibration patterns (e.g., the display notification for camera and different vibration constant vs. chopped) for microphone and location sensor. patterns for microphone and location sensor. Vibration achieves the highest success rate in all environ- 8. REFERENCES ments, including dark environments, and causes less annoy- [1] Dendroid: Android Trojan Being Commercialized. ance compared to sound. Moreover, user can recognize dif- http://blog.trustlook.com/2014/03/20/ ferent vibration patterns. For these two sensors, textual dendroid-android-trojan-commercialized/. [2] How the NSA can ’turn on’ your phone remotely. http://money. 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