September 24, 2018

Can Classes on Field Experiments Scale? Lessons from SOC412

Last semester, I taught a Princeton undergrad/grad seminar on the craft, politics, and ethics of behavioral experimentation. The idea was simple: since large-scale human subjects research is now common outside universities, we need to equip students to make sense of that kind of power and think critically about it.

In this post, I share lessons for teaching a class like this and how I’m thinking about next year.

Path diagram from SOC412 lecture on the Social Media Color Experiment

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Demystifying The Dark Web: Peeling Back the Layers of Tor’s Onion Services

by Philipp Winter, Annie Edmundson, Laura Roberts, Agnieskza Dutkowska-Żuk, Marshini Chetty, and Nick Feamster

Want to find US military drone data leaks online? Frolick in a fraudster’s paradise for people’s personal information? Or crawl through the criminal underbelly of the Internet? These are the images that come to most when they think of the dark web and a quick google search for “dark web” will yield many stories like these. Yet, far less is said about how the dark web can actually enhance user privacy or overcome censorship by enabling anonymous browsing through Tor. Recently, for example, Brave, dedicated to protecting user privacy, integrated Tor support to help users surf the web anonymously from a regular browser. This raises questions such as: is the dark web for illicit content and dealings only? Can it really be useful for day-to-day web privacy protection? And how easy is it to use anonymous browsing and dark web or “onion” sites in the first place?

To answer some of these pressing questions, we studied how Tor users use onion services. Our work will be presented at the upcoming USENIX Security conference in Baltimore next month and you can read the full paper here or the TLDR version here.

What are onion services?: Onion services were created by the Tor project in 2004. They not only offer privacy protection for individuals browsing the web but also allow web servers, and thus websites themselves, to be anonymous. This means that any “onion site” or dark web site cannot be physically traced to identify those running the site or where the site is hosted. Onion services differ from conventional web services in four ways. First, they can only be accessed over the Tor network. Second, onion domains, (akin to URLs for the regular web), are hashes over their public key and consist of a string of letters and numbers, which make them long, complicated, and difficult to remember. These domains sometimes contain prefixes that are human-readable but they are expensive to generate (e.g. torprojectqyqhjn.onion). We refer to these as vanity domains. Third, the network path between the client and the onion service is typically longer, meaning slower performance owing to longer latencies. Finally, onion services are private by default, meaning that to find and use an onion site, a user has to know the onion domain, presumably by finding this information organically, rather than with a search engine.

What did we do to investigate how Tor users make use of onion services?: We conducted a large scale survey of 517 Tor users and interviewed 17 Tor users in depth to determine how users perceive, use, and manage onion services and what challenges they face in using these services. We asked our participants about how they used Tor’s onion services and how they managed onion domains. In addition, we asked users about their expectations of privacy and their privacy and security concerns when using onion services. To compliment our qualitative data, we analyzed “leaked” DNS lookups to onion domains, as seen from a DNS root server. This data gave us insights into actual usage patterns to corroborate some of the findings from the interviews and surveys. Our final sample of participants were young, highly educated, and comprised of journalists, whistleblowers, everyday users wanting to protect their privacy to those doing competitive research on others and wanting to avoid being “outed”. Other participants included activists and those who wanted to avoid government detection for fear of persecution or worse.

What were the main findings? First, unsurprisingly, onion services were mostly used for anonymity and security reasons. For instance, 71% of survey respondents reported using onion services to protect their identity online. Almost two thirds of the survey respondents reported using onion services for non-browsing activities such as TorChat, a secure messaging app built on top of onion services. 45% of survey participants had other reasons for using Tor such as to help educate users about the dark web or for their personal blogs. Only 27% of survey respondents reported using onion services to explore the dark web and its content “out of curiosity”.

Second, users had a difficult time finding, tracking, and saving onion links. Finding links: Almost half of our survey respondents discovered onion links through social media such as Twitter or Reddit or by randomly encountering links while browsing the regular web. Fewer survey respondents discovered links through friends and family. Challenges users mentioned for finding onion services included:

  • Onion sites frequently change addresses and so often onion domain aggregators have broken and out of date links.
  • Unlike traditional URLS, onion links give no indication of the content of the website so it is difficult to avoid potentially offensive or illicit content.
  • Again, unlike traditional URLS, participants said it is hard to determine through a glance at the address bar if a site is the authentic one you are trying to reach instead of a phishing site.

A frequent wish expressed by participants was for a better search engine that is more up to date and gives an indication of the content before one clicks on the link as well as authenticity of the site itself.

Tracking and Saving links: To track and save complicated onion domains, many participants opted to bookmark links but some did not want to leave a trace of websites they visited on their machines. The majority of other survey respondents had ad-hoc measures to deal with onion links. Some memorized a few links and did so to protect privacy by not writing the links down. However, this was only possible for a few vanity domains in most cases. Others just navigated to the places where they found the links in the first place and used the links from there to open the websites they needed.

Third, onion domains are also hard to verify as authentic. Vanity domains: Users appreciated vanity domains where onion services operators have taken extra effort and expense to set up a domain that is almost readable such as the case of Facebook’s onion site, facebookcorewwwi.onion. Many participants liked the fact that vanity domains give more indication of the content of the domain. However, our participants also felt vanity domains could lead to more phishing attacks since people would not try to verify the entire onion domain but only the readable prefix. “We also get false expectations of security from such domains. Somebody can generate another onion key with same facebookcorewwwi address. It’s hard but may be possible. People who believe in uniqueness of generated characters, will be caught and impersonated.” – Participant S494

Verification Strategies: Our participants had a variety of strategies such as cutting and pasting links, using bookmarks, or verifying the address in the address bar to check the authenticity of a website. Some checked for a valid HTTPS certificate or familiar images in the website. However, a over a quarter of our survey respondents reported that they could not tell if a site was authentic (28%) and 10% did not even check for authenticity at all. Some lamented this is innate to the design of onion services and that there is not real way to tell if an onion service is authentic epitomized by a quote from Participant P1: “I wouldn’t know how to do that, no. Isn’t that the whole point of onion services? That people can run anonymous things without being able to find out who owns and operates them?”

Fourth, onion lookups suggest typos or phishing. In our DNS dataset, we found similarities between frequently visited popular onion sites such as Facebook’s onion domain and similar significantly less frequently visited websites, suggesting users were making typos or potentially that phishing sites exist. Of the top 20 onion domains we encountered in our data set, 16 were significantly similar to at least one other onion domain in the data set. More details are available in the paper.

What do these findings mean for Tor and onion services? Tor and onion services do have a part to play in helping users to protect their anonymity and privacy for reasons other than those usually associated with a “nefarious” dark web such as support for those overcoming censorship, stalking, and exposing others’ wrong-doing or whistleblowing. However, to better support these uses of Tor and onion services, our users wanted onion service improvements. Desired improvements included more support for Tor in general in browsers, improvement in performance, improved privacy and security, educational resources on how to use Tor and onion services, and finally improved onion services search engines. Our results suggest that to enable more users to make use of onion services, users need:

  • better security indicators to help them understand Tor and onion services are working correctly
  • automatic detection of phishing in onion services
  • opt in publishing of onion domains to improve search for legitimate and legal content
  • better ways to track and save onion links including privacy preserving onion bookmarking.

Future studies to further demystify the dark web are warranted and in our paper we make suggestions for more work to understand the positive aspects of the dark web and how to support privacy protections for everyday users.

You can read more about our study and its limitations here (such as the fact our participants were self-selected and may not represent those who do use the dark web for illicit activities for instance) or skim the paper summary.

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.