User- Controllable Privacy and Security for Pervasive Computing Jason I. Hong Carnegie Mellon University

The Problem Mobile devices becoming integrated into everyday life –Mobile communication –Sharing location information with others –Remote access to home –Mobile e-commerce Managing security and privacy policies is hard –Preferences hard to articulate –Policies hard to specify –Limited input and output Leads to new sources of vulnerability and frustration

Difficult to Build Usable Interfaces (a)(c)

Our Goal Develop better UIs for managing privacy and security on mobile devices –Simple ways of specifying policies –Clear notifications and explanations of what happened –Better visualizations to summarize results –Machine learning for learning preferences –Start with small evaluations, continue with large-scale ones Large multi-disciplinary team and project –Six faculty, 1.5 postdocs, six students –Roughly 1 year into project

Application Domains Contextual Instant Messaging People Finder Access Control to resources Some Challenges –Not being burdensome or annoying –Finding right balance of expressiveness and simplicity –Helping users understand capabilities and limitations –Providing enough value so that people will use our apps! Security & privacy our main concern, but not to users

Outline Motivation Contextual Instant Messaging People Finder Access Control to Resources

Contextual Instant Messaging Facilitate coordination and communication by letting people request contextual information via IM –Interruptibility (via SUBTLE toolkit) –Location (via Place Lab wifi positioning) –Active window Developed a custom client and robot on top of AIM –Client (Trillian plugin) captures and sends context to robot –People can query imbuddy411 robot for info “howbusyis username” –Robot also contains privacy rules governing disclosure

Contextual Instant Messaging Privacy Mechanisms Web-based specification of privacy preferences –Users can create groups and put screennames into groups –Users can specify what each group can see

Contextual Instant Messaging Privacy Mechanisms Notifications of requests

Contextual Instant Messaging Privacy Mechanisms Social translucency

Contextual Instant Messaging Privacy Mechanisms Audit logs

Contextual Instant Messaging Evaluation Recruited ten people for two weeks –Selected people highly active in IM (ie undergrads ) –Each participant had ~90 buddies and 1300 incoming and outgoing messages per week Notified other parties of imbuddy411 service –Update AIM profile to advertise –Would notify other parties at start of conversation Any predictions of results?

Contextual Instant Messaging Results Total of 242 requests for contextual information –53 distinct screen names, 13 repeat users

Contextual Instant Messaging Results 43 privacy groups, ~4 per participant –Groups organized as class, major, clubs, gender, work, location, ethnicity, family –6 groups revealed no information –7 groups disclosed all information Only two instances of changes to rules –In both cases, friend asked participant to increase level of disclosure

Contextual Instant Messaging Results Likert scale survey at end –1 is strongly disagree, 5 is strongly agree –All participants agreed contextual information sensitive Interruptibility 3.6, location 4.1, window 4.9 –Participants were comfortable using our controls (4.1) –Easy to understand (4.4) and modify (4.2) –Good sense of who had seen what (3.9) Participants also suggested improvements –Notification of offline requests –Better notifications to reduce interruptions (abnormal use) –Better summaries (“User x asked for location 5 times today”)

Contextual Instant Messaging Current Status Preparing for another round of deployment –Larger group of people –A few more kinds of contextual information Developing privacy controls that scale better –More people, more kinds of information

Outline Motivation Contextual Instant Messaging People Finder Access Control to Resources

People Finder Location useful for micro-coordination –Meeting up –Okayness checking Developed phone-based client –GSM localization (Intel) Conducted studies to see how people specify rules (& how well) See how well machine learning can learn preferences

People Finder Machine Learning Using case-based reasoning (CBR) –“My colleagues can only see my location on weekdays and only between 8am and 6pm” –It’s now 6:15pm, so the CBR might allow, or interactively ask Chose CBR over other machine learning –Better dialogs with users (ie more understandable) –Can be done interactively (rather than accumulating large corpus and doing post-hoc)

People Finder Study on Preferences and Rules First conducted informal studies to understand factors important for location disclosures –Asked people to describe in natural language –Social relation, time, location –“My colleagues can only see my location on weekdays and only between 8am and 6pm”

People Finder Study on Preferences and Rules Another study to see how well people could specify rules, and if machine learning could do better –13 participants (+1 for pilot study) –Specify rules at beginning of study –Presented a series of thirty scenarios –Shown what their rules would do, asked if correct and utility –Given option to change rule if desired

People Finder Study on Rules

People Finder Results – User Burden Mean (sec) Std dev (sec) Rule Creation Rule Maintenance Total

People Finder Results – Accuracy

People Finder Current Conclusions Roughly 5 rules per participant Users not good at specifying rules –Time consuming & low accuracy (61%) even when they can refine their rules over time (67%) –Interesting contrast with imbuddy411, where people were comfortable Possible our scenarios biased towards exceptions CBR seems better in terms of accuracy and burden Additional experiments still needed

People Finder Current Work Small-scale deployment of phone-based People Finder with a group of friends –Still needs more value, people finder by itself not sufficient –Trying to understand pain points on next iteration Need more accurate location –GSM localization accuracy haphazard Integration with imbuddy411 –Smart phones expensive, IM vastly increases user base

Outline Motivation Contextual Instant Messaging People Finder Access Control to Resources

Grey – Access Control to Resources Distributed smartphone-based access control system –physical resources like office doors, computers, and coke machines –electronic ones like computer accounts and electronic files –currently only physical doors Proofs assembled from credentials –No central access control list –End-users can create flexible policies

Grey Creating Policies Proactive policies –Manually create a policy beforehand –“Alice can always enter my office” Reactive policies –Create a policy based on a request –“Can I get into your office?” –Grey sees who is responsible for resource, and forwards Might select from multiple people (owner, secretary, etc) –Can add the user, add time limits too

Grey Deployment at CMU 25 participants (9 part of the Grey team) Floor plan with Grey-enabled Bluetooth doors

Grey Evaluation Monitored Grey usage over several months Interviews with each participant every 4-8 weeks Time on task in using a shared kitchen door

Grey Results of Time on Task of a Shared Kitchen Door

Grey Surprises Grey policies did not mirror physical keys –Grey more flexible and easier to change Lots of non-research obstacles –user perception that the system was slow –system failures causing users to get locked out –need network effects to study some interesting issues Security is about unauthorized users out, our users more concerned with how easy for them to get in –never mentioned security concerns when interviewed

Grey Current work Iterating on the user interfaces –More wizard-based UIs for less-used features Adding more resources to control Visualizations of accesses –Relates to abnormal situations noted in contextual IM

Grey Current work in Visualizations

Concluding Remarks User-controllable privacy and security for three apps –Contextual instant messaging –People Finder –Grey distributed access control system Common threads –Simpler ways of specifying policies –Better notifications and explanations –Better visualizations –Machine learning for learning preferences

Concluding Remarks Some early lessons –Many indirect issues need to be addressed to study usable privacy and security (value proposition, network effects) –People seem willing to use apps if good enough control and feedback for privacy and security –Lots of iterative design needed

Acknowledgements NSF Cyber Trust Grant CNS ARO DAAD ("Perpetually Available and Secure Information Systems") to CMU’s CyLab Source:

People Finder Results – Accuracy