With the increasing number of Internet of Things (IoT) devices in smart office buildings, building management systems (BMS) are utilized to improve energy efficiency and occupant comfort by automatically managing indoor environmental conditions, such as temperature, humidity, and lighting conditions, based on occupant behavior and preferences1,2,3,4,5,6,7,8. As research in this area has been increasing over recent years, there is a clearer need to understand occupant behavior and human-building interaction9,10. While increased research on occupant behavior monitoring has led to significant insights into building energy management, occupant comfort, and well-being, most, if not all, of these objectives are founded upon collecting data from the occupants within the built environment.

The sensors embedded in smart buildings (including commercial and residential buildings) fall into environmental sensing, occupancy detection and energy sensing categories with varying levels of intrusiveness11,12,13. However, despite the categories of sensors and whether they are “intrusive” or “non-intrusive,” through advancements in machine learning and signal processing, personal information can be inferred from data collected by the so-called non-intrusive sensors14. A study found that most participants believed in some monitoring and tracking at work, while some explicitly expressed concern about data inferences that would reveal personal information15. Occupants may not even be aware of what information is being collected and how it is used as consent is often implicit16,17. For instance, an indoor air quality sensor, designed to monitor carbon dioxide (\(CO_2\)) and total volatile organic compounds (TVOC) levels in the space for the health of occupants, can reveal information such as whether someone is in the space, how many people are present, what general activities they might be doing (e.g., in a meeting, eating, etc.) and when they arrive and leave their space17,18,19,20. Such inferences of occupant activities can exponentially grow as more modalities are fused together. The lack of awareness of data inferences coupled with implicit consent means that people using a smart office building space lack control of their privacy. In fact, a study found that most participants believed in some monitoring and tracking at work, while some explicitly expressed concern about data inferences that would reveal personal information15. The concern of data inferences is contrasted by interviews conducted by Zheng et al.21, which found skepticism for privacy risks posed by non-audio or video (A/V) data in smart homes.

The lack of awareness extends to the data collection processes as well. A previous study by Harper et al. found that while people were aware of some data collection, they lacked confidence in their knowledge of the process in smart buildings15. Another study suggested that there are different areas of privacy sensitivity in smart homes compared to smart buildings due to potential embarrassment in homes and the addition of potential consequences in buildings22. Potential consequences can be explained through the concern that people in positions of authority can use the data against their subordinates23,24. The different user behaviors and power dynamics between occupants of a smart office building motivates further research into user perceptions of privacy within these buildings.

With the complex nature of privacy, many studies are conducted to define privacy norms despite no central definition of privacy16,22. Nissenbaum developed the theory of privacy as contextual integrity, where the protection of privacy is tied to social contexts and the norms defined within it25,26. It posits that to evaluate the preservation of privacy, there are five parameters of information flows to consider – the information subject, information type, sender, recipient, and transmission principle. It has been shown that context plays an important role in user perceptions and attitudes toward privacy22. Thus, it is important to elucidate the components that contribute to the privacy sensitivity of an area and the contexts in smart office buildings.

In summary, previous works were primarily in smart homes, and those in smart office buildings were mostly through surveys that did not allow for in-depth questioning of participants15,21,22,23,24. Additionally, for studies on smart homes, the findings may not translate to smart office buildings wholly. Collectively, past studies show that semi-structured interviews effectively elicit perceptions and details of users’ privacy concerns in smart homes. However, unlike survey studies, semi-structured interviews can capture the complexity of data collection and human-building interactions in offices. It allows for deeper insight into participant privacy perception and decision-making process that cannot be captured through surveys. Our study builds on the previous works by conducting semi-structured interviews that thoroughly assess the perceptions of privacy and the mental models of data collection and usage in a smart office building space. This study focuses on enhancing the knowledge of the aforementioned gaps by performing a detailed interview with the occupants of a smart office building.

Consequently, this study aims to answer the following research questions (RQ):

  • RQ1: What contexts are people sensitive in sharing their data?

  • RQ2: How does perceived potential benefit/reward alter privacy attitudes?

  • RQ3: How do perceptions of smart office building IoT privacy align with reality?


We conducted semi-structured interviews with twenty-four residents of a smart office building situated within a university as described by Fig. 1. A smart office building space is any office with granular sensing capabilities through IoT sensors and actuations based on data collected from the sensors to optimize the operation of its system, such as lighting or heating and cooling systems. The setting of the study meets the definition of a smart office building as it implements building automations based on data collected from various sensors throughout the space. The interviews aim to understand the residents’ awareness, perception, and data privacy preferences within a smart office building space. The study was approved by the Institutional Review Board for Social & Behavioral Sciences at the University of Virginia (IRB-SBS Protocol #4975). All participants reviewed the informed consent and agreed to participate in the study. All methods were performed in accordance with the relevant guidelines and regulations.

Figure 1
figure 1

The overview of the smart office building space that participants work in.

The targeted space comprises of more than 250 residents (faculty, graduate research assistants, and staff). The space is designed similarly to an open space commercial office building with individual cubicles, conference rooms, offices, and a common space. This space is a Cyber-Physical-Systems (CPS) research facility. As a result, the residents are familiar with the different areas of CPS, security, and policy, so we consider the group to be highly educated about autonomous systems, hardware for IoT, smart cities, and smart health.


A recruitment email was sent via the listserv for residents of the smart office space, which includes students and staff. Twenty-four participants were interviewed between April and May 2022. Most participants (83.3%) were graduate students, and the remaining were staff and faculty at the University of Virginia, representing the overall population of the space. The criterion of “saturation of information” was used to help judge whether enough participants were interviewed where interviewers begin to hear the same information and no new information is learned27. We first reached data saturation after interviewing fifteen participants, where no new information was presented, but we interviewed nine more to avoid an early conclusion; thus, no further interviews were conducted after twenty-four participants. There were 11 female and 11 male participants, while two participants preferred not to disclose their gender identity. The age distribution of the participants was as follows: 18 to 24 years (n=4), 25-34 years (n=17), 45-54 years (n=1), and preferred not to answer (n=1).

Interview procedure

They were asked to answer a few demographic questions such as gender, age, and role at the University. Participants could choose to have the interview online, via Zoom, or in person, where the audio was recorded. Each participant was compensated with a $10 gift card for participating in this study. Interviews were semi-structured and focused on questions as listed:

  1. 1.

    What smart devices do you own?

  2. 2.

    What were your criteria in deciding what devices to buy?

  3. 3.

    Could you point out the areas that you frequent?

  4. 4.

    Which areas would you care about data being collected? Why?

  5. 5.

    What data do you think is being collected in the [name of workplace]?

  6. 6.

    Who do you think has access to the data?

  7. 7.

    Under what circumstances would you not be okay with data being collected and analyzed?

  8. 8.

    What if you benefit from data being collected?

  9. 9.

    In the context of smart buildings and the work environment, what is your definition of privacy? security?

  10. 10.

    Would you be willing to take steps to protect your privacy?

The flow of the questions was designed not to prime the participants towards privacy-related answers and to mitigate biased answers, so the word privacy was not mentioned until the end of the interview. Two interviewers participated in each interview and worked together to follow up on points of interest that arose in conversation and ensure all interview questions were answered. Example points of interest were when participants mentioned privacy early on in the interviews or when the subject of identifiability of occupants was brought up. Participants were asked about IoT devices they own and their purchasing criteria to assess familiarity with IoTs. The rest of the interview focused on their experiences with IoTs within the smart office building at the University as well as their preferences, comfort, boundaries, and awareness pertaining to the collection of different data modalities – audio, video, and environmental. We also asked for each individual’s definition of privacy and security in the context of a smart building, such as the one they occupy for work, and their willingness to take steps to ensure their privacy.

Figure 2
figure 2

Interview responses were analyzed and categorized with codes. The initial codes were pooled from the separate coding of three researchers and refined through discussion and iterative analysis of the transcripts. The individual codes formed three categories – Rewards & Utility, User Knowledge, and Data Features – that fall under a larger umbrella of data privacy preferences.


We followed a standard procedure for analyzing qualitative data from interviews described by Seidman in a guide for qualitative research27. In summary, two researchers independently coded each transcription of the interviews, while a third researcher reviewed the codes and assisted in resolving conflicting opinions. A preliminary codebook is formed, which is a list of the words and phrases that encapsulate the data privacy preferences of users and help answer the research questions. The transcripts are then iteratively reviewed line by line and coded, then discussed among the research group to form the final code book. The final codebook consisted of three high-level categories, Rewards & Utility, User Knowledge, and Data Modality Features, with two to four codes each, as described by Fig. 2. The final codebook was used to analyze and re-code the interviews to align with the refined codes. Through the three categories and the contained codes, several themes emerged. Any conflicts and questions from coding the interviews were resolved through discussions between three researchers.


This section describes the components influencing occupants’ privacy preferences in a smart office building from semi-structured interviews with 24 participants. Two components contributing to data privacy preferences emerged from our interviews: data modality features and personal features. Data modality features focus on the context surrounding data collection with the respective modality. In contrast, personal features are the individual’s values and the mental models of data collection that influence their privacy preferences.

Figure 3
figure 3

There is a hierarchy of data modality features that influence people’s privacy preferences in smart buildings. Spatial context is the most influential, with decreasing influence from top to bottom.

Data modality features

People’s willingness for data collection depended largely on 1) the modality being collected and 2) the features of that modality, which include spatial, security, and temporal context. Modality is the different types of data collected (i.e., environmental, audio, and video). For each modality, spatial context is defined as the physical space and who is in that space. In the setting of this study, an individual’s workspace could be a desk with dividers in an open-plan office or an office with transparent glass walls designed for a single occupant. Security context refers to the data access protocols, encryption protocols, and the level of anonymity. There are two aspects to the temporal context: the time of day and the age of the data being used. The interviews have shown a hierarchical order to the data modality features where participants have a specific privacy preference given each modality’s spatial, security, and temporal context, as seen in Fig. 3.

Modality sensitivity scale

For the purposes of the study and based on the responses, modality can be grouped into environmental, audio, and video data categories. There is a sensitivity scale for the data modalities where our participants are less sensitive and more comfortable with environmental data being collected, shown in Fig. 4. Overall, nearly everyone (n=23) did not mind indoor environmental quality (IEQ) data being collected. IEQ was broken down into smaller components of temperature and humidity, \(PM_2.5\), \(CO_2\), and TVOCs, which were then plotted in Fig. 4 according to current research in smart building privacy17,18,19,20,28. In contrast, people are most sensitive to audio recordings and are against the collection of audio recordings (n=24). Video data also has high sensitivity but not as much as audio recordings, as some (n=6) accept video data if collected in public spaces, as seen in Fig. 4. Furthermore, participants (n=8) indicated they were more comfortable and accepting of noise levels rather than audio recording data because they viewed noise levels as less intrusive compared to recordings of conversations:

“I feel like noise level would be okay. But I feel like, uh, audio recording, like in confined spaces, maybe not.” (P9)

When asked about what data modality they would be okay with and where many cited A/V recording data as being intrusive and feelings of discomfort:

“But I wouldn’t want like recordings of audio from really anywhere because you have conversations with people. And if you feel like all your conversations are being recorded, that would be kind of creepy.” (P8)

A/V data appeared to be the bottom line for many people and had the most restrictions compared to environmental data, particularly audio recordings. Individual sentiments towards data collection that borders on surveillance are negatively viewed. However, there is also a spatial context component in the sensitivity of the data modality toward deeming what data collection is acceptable.

Figure 4
figure 4

Participant responses have shown different levels of sensitivity towards the various data modalities. The data modalities can be plotted on a scale comparing sensitivity to the information richness of the data.

Spatial context

Spatial context is defined as where the individual is, who they interact with at the time of data collection, and what activity they are engaged with. Many participants define their data collection preferences with respect to the spatial context – primarily by their designated workspace or public areas – and the modality sensitivity scale.

Half (n=12) of the participants said they would not be okay with A/V recording at their workspace. In comparison, only one individual indicated that they are okay with audio recording at their workspace because they do not talk much there. Even in open-plan offices, individual workspaces are valued and considered personal spaces. Two participants that stipulated they would not be okay with A/V at their workspace also preferred not to have any A/V data collected in individually occupied spaces around the building (e.g., single occupancy offices), including common spaces, if they were the only person there. This posits that public common areas with a single occupant are considered more private overall. Nearly everyone (n=23) was okay with environmental data collected everywhere except for one individual, who was not okay with any data collected at the workspace, which conveys the acceptable nature of environmental data.

Approximately 54% (n=13) answered that they would be okay with either audio or video in other more public spatial contexts, including corridors, common spaces, and conference rooms. Three individuals that were okay with video data being collected cited safety and protection of valuables as the primary purpose:

“I can understand that like video is kind of helping with like, surveillance is kind of safety.” (P22)

Others described that audio or video, or both, were okay in more public spatial contexts because it is in the presence of others:

“I feel fine about [audio and video] being collected in the [public] ... I’m normally aware that I’m in a more public area, whereas if I’m in my actual office, I don’t know how I feel about data being collected, audio data and video data, in my office.” (P15)

The underlying justification is that when one speaks in a common space, others can also hear it, which can be equated to “something or someone” listening in through audio recordings. People are cognizant when in a public space and recognize that they adopt a filter for what they say.

Security context

Security context can be defined as who has access to the data and the level of anonymity in the data. The level of anonymity refers to how aggregated the data is or the ease of de-identification. In expressing preferences for data collection, some said they are only okay with recording audio data if it can be aggregated into sound levels, which goes back to the modality sensitivity scale. Over 62% (n=15) explicitly stated that they did not want any identifiable data to be collected. Over 29% (n=7) of participants expressed the preference of being able to control who has access to data, while the majority (n=20) were okay with researchers and relevant parties approved by the IRB to have access to their data. Even among those that did not explicitly express approval of researcher use, they indirectly approved as they were okay with residents and people relevant to the smart building (i.e., facility management, researchers) having access to the data.

Aside from who has access and the level of anonymity, another aspect of security is associated with data storage protocols and how access is managed. Just over half (n=13) of the interviewees discussed data security, but only a few (n=3) expressed security measures as a prerequisite or as part of their consideration in data collection. The interviews showed that participants cared most about who has access and the level of anonymity, while data storage and how data can be accessed are not thought of as much.

Temporal context

Temporal context is defined as when the data collection occurs and the age of data. 75% (n=18) of participants did not think that the age of data matters, but some indicated that using older data is more acceptable. The age of data did not change how acceptable it was for A/V data to be collected, as a few expressed fear of harmful consequences. However, some participants expressed that A/V data may be more acceptable outside work hours. One individual justified video being acceptable outside of work hours to monitor the equipment within the space in case of intruders.

Personal features

Other than data modality features, personal features influence individuals’ privacy preferences and acceptability towards data collection. Specifically, the interviews showed definitions of privacy and security, awareness, and rewards as the primary features that vary among individuals regarding privacy preferences.

Participants’ definition of privacy and security

The participants often blurred the distinctions between privacy and security in the smart office spaces, and most did not have readily available definitions. However, providing examples as part of their responses helped verbalize their definitions, which goes on to demonstrate that inability to define privacy does not equate to not caring about privacy. Most defined privacy as data that is not identifiable:

“As long as the information cannot be used to identify specific person...” (P3)

One participant also recognized that the definition of privacy is subject to change due to the technologies and their capabilities:

“This is an ever evolving space, so we have to deal with issues as they come up right now. I think that definition is a pretty issue free one, but we will see in the future so that’s just why I say for now.” (P4)

The definitions of privacy varied, but almost all revolved around information tied to the individual or space belonging to the individual. When asked for their definition of security in a smart office space like the one they work in, some participants wanted clarification on whether it was with respect to data security or building security. In response, we left that up to the participants to determine what they think is most suitable. Some participants only provided a definition that references the physical security and safety of the building, while data was not discussed:

“That’s a hard one for me because I’m in an close space, [so] security is not something that I really worry about...I don’t know how it will change if it’s in a smart building or not for me.” (P18)

The latter response suggests no difference in the definition of security between a smart building as compared to a conventional building. In contrast, security definitions that fall under data security were primarily about authorized access, preventing unauthorized users from accessing data, and encryption. Some participants also expressed that security in the smart office building is composed of two parts – building security for the safety of occupants and data security for protecting sensitive information:

“Safety is the word that comes up first [for] security... So security, I think of [is] you know, physical safety and then security [is] also protection against viruses and people hacking the system and things like that.” (P14)

As participants were asked for the definitions of privacy and security successively, some participants provided the same response to both questions or noted a minimal difference between the two. Privacy was more focused on the individual and the space they occupy, while security had more to do with the safety of the building. The differences in how participants define privacy and security are also presented in differences in data preferences.

Awareness of data modality features and inferences

All participants were aware of environmental data being collected – namely, \(CO_2\), temperature, and humidity. A few indoor environmental quality (IEQ) metrics collected in the space that a few participants did not mention were TVOCs, \(PM_{2.5}\), voltage, and noise levels. It should be noted that the awareness of IEQ data collection can be attributed to the display of numerous IEQ sensors around the space if recognized. Beyond these metrics, participants are uncertain if any other data is collected, like A/V data. Noise levels and audio recordings were a point of contention as some participants pointed out that noise levels could mean that audio recordings are collected.

As most participants were concerned with collecting A/V data, almost everyone found environmental data to be completely acceptable. The majority were averse to identifiable data, but it was mostly referring to A/V data as only a few (n=3) said they were okay with data inferences from IEQ sensors while some (n=4) said they would not want data inferences or at least not without consent or authorization. Most participants were unaware of the risks posed by innocuous sensors, such as IEQ sensors. The contrast is apparent in responses as people oppose A/V data due to tracking behavior but are very accepting of environmental data. One participant defined identifiability as data tagged with the individual’s HIPAA identifiers (i.e., name, date of birth, etc.).

The participants that were accepting of environmental data were asked if their response would change if inferences could be made from environmental data, such as their behaviors and routines; there were mixed responses, most with uncertainty. What participants see as identifiable data also seems to vary, but A/V data are indisputably perceived as identifiable. In contrast, one participant supports the use of data inferences to maximize the potential of a smart office building. The privacy risk of data inferences is not as tangible as A/V data, where one would feel tracked and monitored.

Rewards & utility

Through the interviews, numerous responses showed that willingness for data collection depended on the trade-offs. Specifically, participants described a cost-benefit analysis of utility and loss of privacy. About 63% (n=15) of participants indicated that well-being insights have minimal to no impact on their willingness for data collection or that it depended on offered utility. To elaborate, well-being insights can be as simple as providing occupants with information regarding the environment or suggestions on which spaces within the building would be optimal with respect to health, comfort, and productivity.

The participants also provided a similar answer with respect to the influence of monetary compensation on willingness for data collection:

“I don’t know if financial compensation really quite does it for me because like money is limited. I can’t imagine it’s gonna be significant.” (P1)

However, 33% indicated that well-being insights would increase their willingness for data collection, including video data, with minimal to no change to the willingness from monetary compensation. However, well-being insights must have a large societal impact, such as cancer diagnosis. To elaborate, P18 is more willing to provide A/V data if cancer could be diagnosed or well-being insights of a similar magnitude. Regardless, participants would still want to weigh the costs and benefits. Even as receptiveness to the collection of video data increased with well-being insights offered, some specified that they would want to know how the data is being used and de-identified. One participant’s response was the following:

“If you can detect my audio to tell me that I have cancer, for instance, I’ll be like ’yea sign me up for that,’ because I think it’s really, it does help me, so, no issues and same for video again ... Can you tell me that I have skin cancer just by looking at my face for instance, or can you tell me that I have poor posture looking at my posture and then doing some gait matching. I would be more inclined, but it would not be a yes until I know and I have done my own sort of pros and cons.” (P18)

However, one participant actively dedicates time to review privacy policies and settings of every application they use but is willing to provide data when monetary compensation is offered. As evidence, this participant also described his participation in a study that collected extensive physiological data through wearables and frequent surveys. Overall, some reward does incentivize data collection, including individuals who claim to be very private.

Outside of privacy preferences, participants also highly value convenience, and it appeared in many of the interviews as part of their decision-making process – from purchasing criteria of personal devices (e.g., smartphone, smart home assistants, etc.) to time spent on privacy settings. Even simple strategies and actions that may preserve privacy will be ignored if it becomes a hindrance. For example, one participant used to tape her webcam due to heightened concern about remote spying through cameras but stopped after it proved too bothersome when she needed to use it. As simple as the action was, it conveys the impedance of the smallest inconvenience in implementing privacy protection behaviors. This notion is reinforced when participants are asked how much time and effort they are willing to spend on steps to secure their privacy:

“I’m sure it is bad, but at least for me, I feel like my desire for privacy is all for convenience. So like the less convenient it is, the more I kind of give on privacy.”

Over half of the participants (n=15) responded that they would want to spend as little time as possible, and the exact time ranged from 0 minutes to 15 minutes per day at most. They also expressed that they prefer a one-time adjustment rather than daily or frequent adjustments. A few of those participants (n=3) went on to elaborate there is no need as there is no threat. In contrast, two participants said they were willing to spend up to 30 minutes, and some (n=4) specified 1-2 hours per day. Most participants would rather spend as little time as possible and prefer a one-time adjustment rather than daily or frequent adjustments.

Figure 5
figure 5

People’s privacy preferences are composed of data modality features and personal features. Data modality features and data inferences contribute to the individual’s awareness. One’s awareness, definitions of privacy and security, and the rewards and utility make up the personal features.


In our study, we conducted semi-structured interviews among occupants of a smart office building to understand privacy perceptions, awareness, and preferences in a smart office building. Through the analysis of the interviews, we found a gap between the perceptions and reality of smart building IoT privacy, as many were uncertain of the data collection processes and the capabilities of data inferences. The interviews allowed us to understand the underlying mechanisms of privacy preferences – personal features, and data modality features. Personal features are intrinsic to the participants: awareness of data modality features and inferences, definitions of privacy and security, and rewards and utility, as shown in Fig. 5.

In data modality features, each data modality has its own spatial, security, and temporal context, respectively ranked from high to low influence. Spatial context, as the most influential, is evident in how people see their workspace as more private and are less comfortable with intrusive data (i.e., A/V) to be collected compared to more public areas of the building. Our results extend previous findings that people are most sensitive with their personal offices22 to open-plan workspaces. The security context is how easily the data could be de-identified (i.e., noise levels compared to audio) and who has access. The importance of data access can be the concern of potential consequences associated with collected data22. The temporal context is when the data is collected and how old it is. Some responses expressed increased acceptability of A/V data outside of work hours, which could be due to a lack of people in the space.

Awareness also extends to data inferences – the possibilities of inferred information from data. Very few participants brought up data inferences and the respective privacy risks. Even as we prompted the participants with inferences that can be made from the environmental data, such as their routines, they remained either skeptical or unconcerned. The lack of concern stands in stark contrast to a study that demonstrated an accuracy of 74.78% in predicting activities in smart buildings29 and 97.8% in smart homes using innocuous motion and IEQ sensors. The opposition to A/V data is because of its ability to track and monitor people, but similar tracking capabilities can also be found in non-A/V sensors used for building functions. Though A/V data can identify occupants, it is important to recognize that environmental sensors can reveal behaviors and routines, which can be used to track, identify and potentially punish employees. While almost all participants had some knowledge of what data was being collected, they all expressed uncertainty in how it would be used. It becomes apparent that there is a gap between the mental model or awareness and the reality of data collection and privacy risks within the building space, which is in line with previous works15,23.

Participants struggle with defining privacy and security as many blur the two concepts but are able to express their privacy preferences. The interviews frame privacy as context-specific, which aligns with Nissenbaum’s theory of privacy as contextual integrity26. The differences between smart office buildings and conventional buildings, as participants know them traditionally, are not obvious, which causes many privacy risks to be overlooked, especially data inferences.

In addition to the participant’s awareness and definition of privacy and security, the available rewards can influence how accepting the individual is towards collecting different data modalities. Our findings show that as the sensor becomes more intrusive and the data becomes more sensitive, the greater the incentive must be. In the interview, there was a distinction between rewards: monetary compensation, well-being insights, and improving building automation as potential benefits. While previous research has shown monetary rewards can motivate many different behaviors, previous works have shown that monetary contributions may not cause people to overlook their privacy concerns or incentivize contributions30,31,32,33. However, it was clear in participant responses that significantly higher compensations are needed as an incentive for more intrusive modalities (i.e., A/V). It is difficult to quantify the loss of privacy as described by Fig. 4, and there is a limit to how much money is available for compensation. In contrast, large societal impact and insights for improved well-being were influential motives for sharing more sensitive data to a greater degree than monetary compensation, which is supported by a previous study with sharing dashcam video data34. It is important to note that some responses from our study highlight that they would still like to know how a specific data modality will provide the insight and whether a less sensitive data modality could provide the same utility with an acceptable margin of loss of information.

Though many participants prefer to spend as little as possible on privacy settings, a few indicated that they were willing to spend from 30 minutes to one to two hours. However, quantifying time is difficult, and people may not want to spend as much time as they specified. Future time perception has been found to influence decision-making and behavior, especially when a future time interval ends in a gain or loss35. Depending on how an individual may perceive the task of protecting privacy as a chore (“negative”) as compared to a beneficial one (“positive”), the time may be longer or shorter. People do care about privacy but do not want to spend time as it should be a given. There have been studies on preserving privacy in sensor data, primarily on handling data after collection and not prior36,37,38.

A limitation of our study was that the participants were highly educated with a specific background in cyber-physical systems, including smart cities and buildings. Since there were significant gaps between the perceptions and reality of smart building privacy found in this participant pool, it warrants further research into people who may not necessarily work in a smart building. Other studies39,40 have found that individual differences, such as demographics and cultural differences, can contribute to different privacy concerns and preferences. The potential influence of demographics on people’s privacy preferences in smart buildings warrants in-depth research with a representative participant pool, as current literature broadly focuses on online privacy. Our study does not evaluate whether participant responses translate to actual behavior, such as whether they will behave as they say when actual rewards are available. In addition, the study does not evaluate privacy preferences longitudinally.

Future work should include large-sample surveys representative of the general American population to assess differences in privacy perception, awareness, and preferences between age, gender, and other demographic factors. Researchers should expand research into personalized default privacy settings that make protecting one’s privacy as intuitive as possible and user-centric. This study shows that privacy preferences are context-specific, so we need flexible systems to adhere to individual preferences. In addition, there needs to be longitudinal data on people’s privacy preferences and perceptions in smart office buildings that will allow insight into how they change over time. To add robustness, there needs to be naturalistic studies into the effects of rewards on willingness for data collection.

The findings of this study can inform building practitioners of issues they should consider in the design of spaces and improve human-building interactions. Sensors are often placed in smart buildings without privacy considerations in the design phase, where privacy is an afterthought. Suppose privacy concerns are known ahead of time, the placement of sensors can be optimized to decrease privacy risk and inform default privacy settings that will protect people’s privacy at the point of data collection.