November 20, 2018

User Perceptions of Smart Home Internet of Things (IoT) Privacy

by Noah Apthorpe

This post summarizes a research paper, authored by Serena Zheng, Noah Apthorpe, Marshini Chetty, and Nick Feamster from Princeton University, which is available here. The paper will be presented at the ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW) on November 6, 2018.

Smart home Internet of Things (IoT) devices have a growing presence in consumer households. Learning thermostats, energy tracking switches, video doorbells, smart baby monitors, and app- and voice-controlled lights, speakers, and other devices are all increasingly available and affordable. Many of these smart home devices continuously monitor user activity, raising privacy concerns that may pose a barrier to adoption.

In this study, we conducted 11 interviews of early adopters of smart home technology in the United States, investigating their reasons for purchasing smart-home IoT devices, perceptions of smart home privacy risks, and actions taken to protect their privacy from entities external to the home who create, manage, track, or regulate IoT devices and their data.

We recruited participants by posting flyers in the local area, emailing listservs, and asking through word of mouth. Our recruiting resulted in six female and five male interviewees, ranging from 23–45 years old. The majority of participants were from the Seattle metropolitan area, but included others from New Jersey, Colorado, and Texas. The participants came from a variety of living arrangements, including families, couples, and roommates. All participants were fairly affluent, technically skilled, and highly interested in new technology, fitting the profile of “early adopters.” Each interview began with a tour of the participant’s smart home, followed by a semi-structured conversation with specific questions from an interview guide and open-ended follow-up discussions on topics of interest to each participant.

The participants owned a wide variety of smart home devices and shared a broad range of experiences about how these devices have impacted their lives. They also expressed a range of privacy concerns, including intentional purchasing and device interaction decisions made based on privacy considerations. We performed open coding on transcripts of the interviews and identified four common themes:

  1. Convenience and connectedness are priorities for smart home device users. These values dictate privacy opinions and behaviors. Most participants cited the ability to stay connected to their homes, families, or pets as primary reasons for purchasing and using smart home devices. Values of convenience and connectedness outweighed other concerns, including obsolescence, security, and privacy. For example, one participant commented, “I would be willing to give up a bit of privacy to create a seamless experience, because it makes life easier.”
  2. User opinions about who should have access to their smart home data depend on perceived benefit from entities external to the home, such as device manufacturers, advertisers, Internet service providers, and the government. For example, participants felt more comfortable sharing their smart home data with advertisers if they believed that they would receive improved targeted advertising experiences.
  3. User assumptions about privacy protections are contingent on their trust of IoT device manufacturers. Participants tended to trust large technology companies, such as Google and Amazon, to have the technical means to protect their data, although they could not confirm if these companies actually performed encryption or anonymization. Participants also trusted home appliance and electronics brands, such as Philips and Belkin, although these companies have limited experience making Internet-connected appliances. Participants generally rationalized their reluctance to take extra steps to protect their privacy by referring to their trust in IoT device manufacturers to not do anything malicious with their data.
  4. Users are less concerned about privacy risks from devices that do not record audio or video. However, researchers have demonstrated that metadata from non-A/V smart home devices, such as lightbulbs and thermostats, can provide enough information to infer user activities, such as home occupancy, work routines, and sleeping patterns. Additional outreach is needed to inform consumers about non-A/V privacy risks.

Recommendations. These themes motivate recommendations for smart home device designers, researchers, regulators, and industry standards bodies. Participants’ desires for convenience and trust in IoT device manufacturers limit their willingness to take action to verify or enforce smart home data privacy. This means that privacy notifications and settings must be exceptionally clear and convenient, especially for smart home devices without screens. Improved cybersecurity and privacy regulation, combined with industry standards outlining best privacy practices, would also reduce the burden on users to manage their own privacy. We encourage follow-up studies examining the effects of smart home devices on privacy between individuals within a household and comparing perceptions of smart home privacy in different countries.

For more details about our interview findings and corresponding recommendations, please read our paper or see our presentation at CSCW 2018.

Full citation: Serena Zheng, Noah Apthorpe, Marshini Chetty, and Nick Feamster. 2018. User Perceptions of Smart Home IoT Privacy. In Proceedings of the ACM on Human-Computer Interaction, Vol. 2, CSCW, Article 200 (November 2018), 20 pages. https://doi.org/10.1145/3274469

Thoughts on California’s Proposed Connected Device Privacy Bill (SB-327)

This post was authored by Noah Apthorpe.

On September 6, 2018, the California Legislature presented draft legislation to Governor Brown regarding security and authentication of Internet-connected devices. This legislation would extend California’s existing reasonable data security requirement—which already applies to online services—to Internet-connected devices.  

The intention of this legislation to prevent default passwords and other egregious authentication flaws is admirable, especially given the extent of documented security vulnerabilities in Internet of things (IoT) devices. Many such vulnerabilities, including default passwords and cleartext data transmissions (e.g., in IoT toys and medical devices discovered by researchers at Princeton), stem from lazy developer practices resulting in devices with minimal (or nonexistent) security or privacy protections. Such flaws could be addressed with well-known best practices that would not place excessive burden on device manufacturers.  With this context in mind, we applaud California’s effort to mandate minimal security and privacy protections for connected devices.

Unfortunately, as critics have pointed out, the wording of the proposed legislation is imprecise, especially regarding “reasonable security features.” Rather than reiterate this criticism, we point out some additional technical limitations of the proposed legislation, focusing on cases where the current language does not properly address security flaws as intended. We hope that these examples will help inform future improvements to this or other IoT security and privacy legislation.

1798.91.04.b.1: “The preprogrammed password is unique to each device manufactured.”
Mandating unique passwords for each device still leaves room for passwords that are still easily guessable. For example, a manufacturer could assign consecutive integers as passwords to all devices and be in compliance, but such passwords could be easily enumerated by an attacker. Related problems have already occurred. In 2015, TP-LINK routers were shipped with unique WiFi passwords derived from the hardware (MAC) addresses of each device. This made it trivial to observe traffic from one of these routers and subsequently guess the WiFi password. Much research has gone into the topic of generating secure passwords, the takeaways of which should be incorporated into any default password prevention law. Ultimately, additional criteria on preprogrammed unique passwords are needed for this bill to provide the intended security benefits.

1798.91.04.b.2: “The device contains a security feature that requires a user to generate a new means of authentication before access is granted to the device for the first time.”
This alternative requirement leaves open the possibility that devices or device features that users never access will not receive unique, secure passwords or other new authentication means. For example, many users never access the administrative interface of their home router (which typically has a different authentication method than the WiFi network itself). If the first login attempt is from an attacker, the attacker could receive the new authentication credentials.

Additionally, this requirement does not address the potential for devices to employ otherwise insecure authentication systems that still generate new credentials upon first access. As an analogy, just because an Internet user creates a new password for each online account does not mean that each of these passwords is necessarily secure. Again, additional criteria on the authentication system are needed.

1798.91.05.b: “‘Connected device’ means any device, or other physical object that is capable of connecting to the Internet, directly or indirectly, and that is assigned an Internet Protocol address or Bluetooth address.”
This extends the purview of the proposed legislation to PCs, laptops, smartphones, servers, and any other computing device that can access the Internet, even though the law is being promoted specifically as a protection for IoT devices.  While we are in favor of additional security and privacy protections on all networked devices, this broad scope may prevent improvements in the specificity of future versions of the legislation which may only be applicable to IoT, mobile, or other subcategory of devices.

1798.91.06.a: “This title shall not be construed to impose any duty upon the manufacturer of a connected device related to unaffiliated third-party software or applications that a user chooses to add to a connected device.”
This leaves unaddressed the complex ecosystem of third-party software that is incorporated into IoT and other Internet-connected devices before they reach the user. As an example, how does this law apply to Android smartphones for which the hardware is made by one manufacturer, the operating system is made by another (Google), and still other companies create mobile applications that come pre-loaded on the phone? The manufacturer of the hardware unlikely implements any authentication visible to the user. Instead, the operating system provides authentication for user access to the phone (usually via a PIN or a biometric fingerprint), while individual apps may have their own authentication measures. Future versions of the bill should clearly specify which criteria apply to the wide range of operating systems, applications, and software libraries from third-parties that provide core security, privacy, and authentication features for many devices.

Final Thoughts
Legal requirements that connected devices avoid well-known and easily fixed security flaws, such as default passwords, are long overdue. Yet, such legislation must recognize the complexity of cybersecurity issues. The above examples demonstrate the type of technical “gotchas” that can hide in well-meaning regulatory language. As California SB-327 or related legislation proceeds, we hope that legislators will consult with academic or industry researchers who have spent considerable effort developing, testing, and refining security and privacy solutions in a wide variety of technology contexts.

Internet of Things in Context: Discovering Privacy Norms with Scalable Surveys

by Noah Apthorpe, Yan Shvartzshnaider, Arunesh Mathur, Nick Feamster

Privacy concerns surrounding disruptive technologies such as the Internet of Things (and, in particular, connected smart home devices) have been prevalent in public discourse, with privacy violations from these devices occurring frequently. As these new technologies challenge existing societal norms, determining the bounds of “acceptable” information handling practices requires rigorous study of user privacy expectations and normative opinions towards information transfer.

To better understand user attitudes and societal norms concerning data collection, we have developed a scalable survey method for empirically studying privacy in context.  This survey method uses (1) a formal theory of privacy called contextual integrity and (2) combinatorial testing at scale to discover privacy norms. In our work, we have applied the method to better understand norms concerning data collection in smart homes. The general method, however, can be adapted to arbitrary contexts with varying actors, information types, and communication conditions, paving the way for future studies informing the design of emerging technologies. The technique can provide meaningful insights about privacy norms for manufacturers, regulators, researchers and other stakeholders.  Our paper describing this research appears in the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies.

Scalable CI Survey Method

Contextual integrity. The survey method applies the theory of contextual integrity (CI), which frames privacy in terms of the appropriateness of information flows in defined contexts. CI offers a framework to describe flows of information (attributes) about a subject from a sender to a receiver, under specific conditions (transmission principles).  Changing any of these parameters of an information flow could result in a violation of privacy.  For example, a flow of information about your web searches from your browser to Google may be appropriate, while the same information flowing from your browser to your ISP might be inappropriate.

Combinatorial construction of CI information flows. The survey method discovers privacy norms by asking users about the acceptability of a large number of information flows that we automatically construct using the CI framework. Because the CI framework effectively defines an information flow as a tuple (attributes, subject, sender, receiver, and transmission principle), we can automate the process of constructing information flows by defining a range of parameter values for each tuple and generating a large number of flows from combinations of parameter values.

Applying the Survey Method to Discover Smart Home Privacy Norms

We applied the survey method to 3,840 IoT-specific information flows involving a range of device types (e.g., thermostats, sleep monitors), information types (e.g., location, usage patterns), recipients (e.g., device manufacturers, ISPs) and transmission principles (e.g., for advertising, with consent). 1,731 Amazon Mechanical Turk workers rated the acceptability of these information flows on a 5-point scale from “completely unacceptable” to “completely acceptable”.

Trends in acceptability ratings across information flows indicate which context parameters are particularly relevant to privacy norms. For example, the following heatmap shows the average acceptability ratings of all information flows with pairwise combinations of recipients and transmission principles.

Average acceptability scores of information flows with given recipient/transmission principle pairs.

Average acceptability scores of information flows with given recipient/transmission principle pairs. For example, the top left box shows the average acceptability score of all information flows with the recipient “its owner’s immediate family” and the transmission principle “if its owner has given consent.” Higher (more blue) scores indicate that flows with the corresponding parameters are more acceptable, while lower (more red) scores indicate that the flows are less acceptable. Flows with the null transmission principle are controls with no specific condition on their occurrence. Empty locations correspond to less intuitive information flows that were excluded from the survey. Parameters are sorted by descending average acceptability score for all information flows containing that parameter.

These results provide several insights about IoT privacy, including the following:

  • Advertising and Indefinite Data Storage Generally Violate Privacy Norms. Respondents viewed information flows from IoT devices for advertising or for indefinite storage as especially unacceptable. Unfortunately, advertising and indefinite storage remain standard practice for many IoT devices and cloud services.
  • Transitive Flows May Violate Privacy Norms. Consider a device that sends its owner’s location to a smartphone, and the smartphone then sends the location to a manufacturer’s cloud server. This device initiates two information flows: (1) to the smartphone and (2) to the phone manufacturer. Although flow #1 may conform to user privacy norms, flow #2 may violate norms. Manufacturers of devices that connect to IoT hubs (often made by different companies), rather than directly to cloud services, should avoid having these devices send potentially sensitive information with greater frequency or precision than necessary.

Our paper expands on these findings, including more details on the survey method, additional results, analyses, and recommendations for manufacturers, researchers, and regulators.

We believe that the survey method we have developed is broadly applicable to studying societal privacy norms at scale and can thus better inform privacy-conscious design across a range of domains and technologies.