Overview
Team: John Sykes, Nate Smith Michael Ji
Class: Design Field Study
Role: Research & Ideation Lead
Duration: 3 Months
Methods: Literature Review, Directed Interviews, Task Analysis, Contextual Inquiry, Open Card Sort, Brainstorming, Sketching,
Behavioral Prototyping, Physical Prototyping, Poster Presentation
Challenge
Adoption of biometrics by mobile phone users is largely driven by convenience rather than security despite significant immediate security benefits. In fact, security and privacy features were among the least valued in our participant pool. I set out to test whether biometric adoption due to ease-of-use had limitations in terms of the user privacy and security by creating an intentionally invasive yet convenient behavioral prototype.
Response
My team developed a behavioral prototype as a critical design response to the increasing ubiquity of biometric security on mobile devices and to understand what limits, if any, millenials had to sharing their biometric information.
Final Prototype Design Response Video
Problem Setting
Secondary Research
Literature Review
In the last decade, smartphones have adopted advanced means of biometric authentication and I set out to discover what behavioral changes have developed as a response to pervasive biometrics and how those behaviors might differ between age groups.
As research lead for the team, I reviewed multiple papers covering the advances and pitfalls of biometric security technology and white papers explaining how some of the more notable technologies, such as Apple’s FaceID worked. I was surprised to find that knowledge-based passwords, such as the common PIN codes and alpha-numeric field entry, are considered insecure by experts because of the human limitations inherent in remembering a code or keyphrase. As we moved into our primary research, we would see I wasn’t the only one with this misconception.
Initial Research Questions
How has the increased fidelity of biometric security in consumer mobile technology affected behavior between age groups?
What is the role of ease in the behaviors
around security?How has pervasive biometric technology in smartphones affected users’ trust around personal privacy and security?
How do different age-groups vary in their opinion of biometrics and smartphone privacy and security?
I hypothesized that the adoption of biometrics would be uneven across generations as many new technologies tend to be adopted by younger generations first.
However, I did wonder if the lower cognitive load and higher accessibility of biometric authentication would drive adoption among older user groups as well. I also wanted to explore any generational differences in trust of the new technology.
Primary Research
Methodology
Open Card Sort, Task Analysis, Semi-Structured Interview, Directed Storytelling
Recruitment and Pivot
The participant breakdown for initial research by age, sex, and mobile platform
Originally, I attempted to recruit an even number of millenials and baby boomers who use biometric authentication on their smartphones.
As recruitment began I realized that we did not have the network or resources to find enough baby boomers to get a satisfactory sample.
We chose to focus solely on how advances in biometrics have shaped millennial behaviors. As a result, we needed to reformulate our main research question:
How has the pervasiveness of biometric authentication in consumer tech affected the behavior of millennials over time?
1. Open Card Sort
Participants were asked to rank 10 commonly valued features of mobile phones including ‘security’ and ‘encryption’ as well as 2 blank “wildcards.”
The goal of this portion of the study was to understand how participants ranked security features of their phones relative to other features like speed, screen quality, and battery life.
I found that security and encryption were generally not valued amongst our participants, validating the secondary research findings that biometric adoption was driven by convenience rather than security.
…security and encryption were generally not valued amongst our participants, validating our secondary research findings…
2. Task Analysis
Participants then demonstrated their typical day-to-day usage habits through directed tasks. I then asked participants about their actions to see the reasoning behind them, if any.
I observed that participants utilized their biometrics as their primary unlock and payment verification method when it was available. Participants noted this was not because it was more secure, but because it was faster and easier - some participants even reported being unsure of their PIN codes due to disuse in favor of biometrics.
…participants utilized their biometrics as their primary unlock and payment verification method when available, not because it was more secure, but because it was faster and easier.
3. Semi-Structured Interview
The final portion of the study is where the bulk of our initial insights came from. I asked about their personal thoughts on privacy and security, directing the conversation more overtly into the topic of biometrics and utilizing directed storytelling to help provide context to their actions.
I learned about participants’ security history, including with breaches such as Equifax. We also learned about their perceptions of their own security, including that the unanimous perception among participants that biometric security was less secure because it was so easy to use.
…the unanimous perception was that biometric security was less secure because it was so easy to use.
Synthesis & Insights
After coding the data, we externalized our observations from primary and secondary research onto Post-It notes, which are easily moved and categorized into themes and then further distilled into insights, which you can find below.
1
People assume knowledge-based passwords are the most secure because of their difficulty. This shows a general lack of technical understanding around passwords and biometrics.
“I think the idea that [Touch ID] is so convenient is tied in my brain to it not being secure.”
2
The inherently private nature of security leads to frequent confirmation bias. Virtually everybody thinks they have “pretty good” password strength, regardless of the actual security level.
“I use 6–7 variations of the [the same] password…my security is pretty good.”
3
There is a general sense that data breaches are inevitable, and little can be done to prevent or prepare for them; thus most people’s security strategy is reactive rather than proactive.
“After the first major [hack]...it becomes less of a shock. And then it’s just like, okay this is what happens.”
4
Fiction is often people’s first exposure to biometrics, which often depict the technology as being compromised in some way. This negative portrayal leads to a lack of confidence.
“Sci-fi movies, where they have a hand scanner...obviously give me a negative bias because the good guy uses the incapacitated bad guy’s hand or something.”
5
Despite general indifference towards security, the average person’s mobile phone security level has in fact improved over time, driven largely by the ease-of-use of biometrics.
“I was skeptical about [TouchID] being that secure...then I did a little bit of research on it. And the way that they store the records of the fingerprint...made me feel better.”
Critical Design Response
Hypothesis
…we hypothesized users would be willing to sacrifice security and privacy if the benefit to convenience was great enough.
The research revealed that users were adopting biometrics despite the perception that the technology is less secure than knowledge-based methods. We knew that ease-of-use was driving this adoption, so we hypothesized users would be willing to sacrifice security and privacy if the benefit to convenience was great enough.
This was a troubling notion, so when we began to conceptualize design responses to address it. Was there a limit to this tradeoff? How much information would users be willing to sacrifice assuming there was a large enough increase convenience? Would users be willing to trust a product that utilized a biometric signature that was even more invasive?
Concepts
Brainstorming, Sketching
The team sketched potential design responses, thinking of ways we could test personal thresholds for biometric data collection. The idea that best embodied our questions was based on research I’d read from MIT, where they were developing a way to recognize and authenticate a person using their brainwave signature.
If there were a widely available method of authentication utilizing brainwaves, it could be even more convenient as it could passively authenticate rather than requiring overt interaction. It could also theoretically be more secure than existing biometric devices as my research revealed that brainwave signatures were even more distinct than commonly used biometric authentication methods like facial recognition and fingerprints. Finally, the lack of any existing popular fiction media portrayals of brain wave authentication meant we would be somewhat insulated from bias resulting from dramatic portrayals of the technology.
Behavioral Prototype: Methodology & Testing
Behavioral Prototype, Semi-Structured Interview
The Prototype
Utilizing spare electronics, a pair of glasses frames, and a remote-controlled Keynote deck, we built a low-fidelity version of our ‘Brainwave Authentication Device.’
Initial worries that our participants would see through the illusion were quickly dispelled as the 6 users (including 1 med student) were convinced of its authenticity.
After obtaining consent, we engaged each participant in an hour long study. We began by telling them we would be testing a prototype device developed at the University of Washington and we were interested in gathering user feedback.
As a baseline and introduction, we asked them questions about their security habits and perceptions of biometrics. We then introduced the product, explaining we were trying to understand the perception the public would have to this type of technology being used for biometric authentication.
Setup and Calibration
We used these images because my research had revealed devices which relies on pattern recognition would need to utilize concrete, universal objects as references rather than those that evoke strong emotions or abstract concepts….
We then had each participant run through a setup flow and a dry run of the unlock process. The setup was comparable to other biometric sensors like Apple’s TouchID, where the user follows on-screen instructions meant to act as a combination of onboarding and biometric data collection.
From the participant’s perspective, this consisted of being shown a 3 different pictures of easily definable objects, such as a pizza slice or a basketball. We used these images because my research had revealed devices which relies on pattern recognition would need to utilize concrete, universal objects as references rather than those that evoke strong emotions or abstract concepts. In a real device those objects wouldn’t be viable for authentication because the brainwave signature could fluctuate as a result of changing emotional connections evoked by that object or idea.
Authentication
A simulation of ThnoughtID in the real world, applied to a mobile phone and a locked door.
The participant was then shown one of the images and the their ‘brainwaves were authenticated’ by Nate who was nearby acting as a note-taker on his laptop. In actuality, he was controlling a Keynote deck as the participant spoke his thoughts about the process and what was happening aloud, giving Nate cues on when to switch to the next screen.
Results and Discussion
The results of our behavioral prototype validated the hypothesis we’d derived from the insights of the previous study:
If participants saw a great enough benefit to convenience, they were willing to sacrifice their personal data to a great extent.
In the initial stages of this project, I was shocked to discover that privacy and security were so easily bargained away by our participants As the project continued onto the second phase, I found myself wondering what those limitations were…and I’m still wondering!
The ThoughtID concept is intentionally intrusive and I expected participants to recoil from the concept as a result. In reality, as we talked with participants we discovered there was initial trepidation, but as they discussed desired potential applications of the technology, participants admitted they would be open to adopting the technology assuming it delivered on the promise. I’m hard-pressed to think of a biometric marker more personal than brainwave signatures and if people are conceivably willing to give up their most personal biometric data in exchange for convenience, it is unlikely there is a threshold where users will reject a similar technology assuming there is a strong enough perceived benefit.
The ubiquity of biometric security on devices is driving better security practices amongst even novice smartphone users. Knowledge-based passwords are inherently less secure due to human cognitive limitations, but many feel they are sufficient due to a combination of confirmation bias and the false perception that harder to remember equates to harder to hack. In fact, knowledge based passwords only offer combinations of 7-8 data points which are often non-random, while many commonly used biometrics offer 1,000s or even 100,000s of data points which don’t need to be remembered and are created by chance outcomes.
Unlike knowledge-based passwords, biometrics are finite and immutable. If your information is stolen there is no way to change something like your fingerprints or brainwave pattern.As biometrics continue to become commonplace, privacy laws need to take this personal data into account and protect consumer biological information from being misused by both governments and companies.
