Transcript Access Control Michelle Mazurek Usable Privacy and Security October 29, 2009 Outline • How do people think about access control? • What and when do.

Access Control
Michelle Mazurek
Usable Privacy and Security
October 29, 2009
Outline
• How do people think about access control?
• What and when do they want to share?
• How do people use current standard access
control mechanisms?
• Discussion: System design guidelines
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Thinking about sharing
• Olson, Grudin, and Horvitz: A study of
preferences for sharing and privacy
• Ahern et al.: Privacy patterns and
considerations in online photo sharing
• Recent study by home security team:
Attitudes, needs and practices for home data
sharing
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Mapping detailed preferences
• Overview survey: “A situation in which you or
another person did not wish to share
information.”
• Selected 19 types of people, 40 types of files
• Detail survey: 30 participants filled out the
resulting grid
• How comfortable sharing? 1 to 5 (or n/a)
• Instantiate each type of person
Olson, Grudin, Horvitz, 2005
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Types of people
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Best friend
Spouse
Parent
Sibling
Adult child
Young child
Extended family
Personal website
Salesperson
Trusted colleague
Manager
Subordinate
Corporate lawyer
Competitor
Company newsletter
People you want to
impress
• Other team
members
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Types of information
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Current location
Current IM status
Buddy list
Calendar entries
Web history
Phone, e-mail
Age
Health status
Credit card number
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Work in progress
Finished work
Successes, failures
Performance review
Salary
E-mail content
Social Security #
Politics/religion
New job application
Results – Sharing preferences
People: High to Low
• Spouse, best friend,
parent
• Extended family,
subordinate, team
members
• Competitor,
personal website,
salesperson
Info: High to Low
• Work e-mail, desk
phone, cell phone,
age, marital status
• Health status,
politics/religion,
work in progress
• Transgression, email content, credit
card, SSN
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Results – Variation in variance
• No variance:
• Always share: work e-mail address, phone # with
spouse, co-workers
• Always share: home phone # with spouse, children
• Never share: credit card number with the public
• Lots of variance:
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Personal items with co-workers (age, health, etc.)
Credit card with parents, grandparents
Pregnancy status with siblings
Work documents with family members
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Variation among people
• Unconcerned, pragmatists, fundamentalists
• Overall, restrict more than share
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Clustering people and information
• Manager, trusted
colleague
• Best friend, family
• Other co-workers
• Spouse
• General public
• E-mail content, credit
card #, transgression
• Failures, salary, SSN
• Home, cell number;
age; marital status;
successes
• Health, religion,
politics
• Work-related stuff
• Work e-mail, phone #
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Mapping clustered preferences
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Discussion and guidelines
• One size doesn’t fit all, but strong clustering
implies manageable categories
• Recommend policy configuration at multiple
precision levels
• Recommend statistical prediction (dynamic
cluster analysis)
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Privacy in online photo sharing
• Collected and analyzed data from 5 months
of real use of Flickr with ZoneTag
• 81 users, uploaded at least 40 photos each
• 36,915 total photos
• Followed up by interviews with users about
their access control decision-making
• Recruited social groups of non-technical people
• 15 participants, not previous Flickr users
• Provided phone, data plan, Flickr Pro; used their
own SIM cards
Ahern et al., 2007
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Flickr and ZoneTag
Flickr
• Public vs. non-public (private, family, friends)
• Public photos are searchable (text labels)
ZoneTag
• Cameraphone app for upload to Flickr
• By default, repeat most recent privacy setting
• User can modify as desired
• Add tags (suggestions, quick entry)
• Location tags added automatically
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Does location predict privacy?
• H1: Some locations are more public than
average; others are more private
• Calculate ratio of public photos to total photos
• For all photos, then individually per location
0.25
Very Private
0.1
0.1
0.25
Private Typical Public
Very Public
• 50% of users: fewer than half “typical” photos
• 19 users: more than half “very” photos
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Does location predict privacy?
• H2: More frequent locations are more private
• Calculate ratio as before; sort by frequency
• Found significant correlation
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Does content predict privacy?
• Hand-classified 1400 frequently-used tags
• Person, location, place, object, event, activity
• Calculate public: non-public per category
• Person significantly more private than rest
• Activity significantly less private than average
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Additional quantitative analysis
• Access setting changed after upload: 7%
• Decisions are not often strongly regretted
• May be related to misuse of default
• Location information suppressed: 2%
• Perhaps no concern about zip-level location
sharing
• Perhaps because users don’t know how to
suppress it
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Interview results
• Conducted after 2 weeks of system use
• Four major themes emerged:
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Results: Security
• Location – near real-time whereabouts
• “If I did something to upset somebody somehow
… and they knew exactly where I lived by looking
at my Flickr photos, that would bother me.”
• Especially with regard to children
• Both content and location
• Fits with quantitative analysis
• Person is more private (children)
• Some locations (home?) are more private
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Results: Identity, Social Disclosure
Identity
• Image content may
be unflattering to
user, others
• May expose private
interests
Social Disclosure
• If someone wasn’t
invited
• When friends are
“doing their, uh,
‘musician things’ …
don’t need any
incriminating
evidence.”
• Conservative family,
pride parade photos
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Results: Convenience
• Using access controls requires friends, family
to sign up for Flickr accounts
• Sometimes it’s easier just to make it public
• Sometimes users want to make certain
photos available to friends of friends
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Results: Location privacy
• Showed participants their aggregate location
data
• Generally unconcerned at zip granularity
• Worries about advertisers
• Changes from pre-study interview
• 17% said would never share
• 50% would share under special circumstances
• In reality, every person shared and no one
suppressed
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Results: Other factors
• Making decisions at capture time
• Unsure how photos will look on the web
• Unsure about subject’s preferences
• Limiting complexity
• Often choose the default for minimal effort
• Dissatisfaction
• Unhappy with all available options
• Choose the best available but remain frustrated
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Discussion and guidelines
• Desired privacy settings often correlate with
location and with content of tags
• Suggestions:
• Use these patterns for prediction,
recommendation, or warning of possible errors
• Make aggregate disclosure visible to users
• Provide social comparison (similar decisions made
by friends) to reveal relevant norms
• Show users how others will view the photos
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Further guidelines
• Decouple visibility from discoverability
• Public settings so no need to register
• Not searchable so disclosure may be limited
• Decouple photo and location visibility
• Independent location disclosure settings
• Encourage a range of access control settings
• Self-censoring limits usage
• Maximizing public photos adds value to system
owner
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Exploring access control at home
• Goal: Understand how people think about
access control
• Wanted to examine several different facets
• Current practices: digital, paper
• Different policy dimensions: person, location,
device, presence, time of day
• Additional features: Logs, reactive policy creation
Mazurek et al., 2009
http://www.pdl.cmu.edu/
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Michelle Mazurek © Oct 09
Designing a user study
• In-situ interviews
• Recruitment via Craigslist, flyers
• Limited to non-programmer households
• Interview family members at home: together, then
individually
• Semi-structured interviews
• Elicit information about people, files
• Ask specific questions as jumping-off points
• Continue with free-form responses
http://www.pdl.cmu.edu/
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Michelle Mazurek © Oct 09
Question structure
• For each dimension, start with a specific
scenario
• Imagine that [a friend] is in your house when you
are not. What kinds of files would you (not) want
them to be able to [view, change]?
• Would it be different if you were also in the [house,
room]?
• Extend to discuss that dimension in general
• Rate concern over specific policy violations:
• 1 = don’t care to 5 = devastating
http://www.pdl.cmu.edu/
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Michelle Mazurek © Oct 09
Data analysis
• Initial rough analysis identified areas of
interest; fed back into later interviews
• Iterative topic and analytic hand coding –
loosely based on Grounded Theory1
• Searchable database format
• Combine codes to develop broader theories;
revisit data as needed
• Results are qualitative
1Razavi
http://www.pdl.cmu.edu/
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and Iverson, 2006
Michelle Mazurek © Oct 09
Study demographics
Households People
Families
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16
Couples
5
10
Roommates
4
11
Total
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37
• Ages 8 to 59
• Wide range of computer skills
http://www.pdl.cmu.edu/
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Michelle Mazurek © Oct 09
Household devices
• Common devices: Laptop, desktop, DVR,
iPod, cell phone, digital camera
• Minimum: 1 desktop, 1 mobile phone, 3 iPods
for a family of four
• Maximum: 22 devices for 3 roommates
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3 laptops, 2 desktops
3 cell phones
Still and video cameras
Video game systems
USB sticks, memory cards, external hard drives
http://www.pdl.cmu.edu/
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Michelle Mazurek © Oct 09
Four key findings
1. People have important data to protect, and
the methods they currently use don’t provide
enough assurance
2. Policies are complicated
3. Permission and control are important
4. Current systems and mental models are
misaligned
http://www.pdl.cmu.edu/
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Michelle Mazurek © Oct 09
F1: Current methods aren’t working
• People worry about sensitive data
• Many potential breaches rated as “devastating”
• Almost all worry about file security sometimes
• Several have suffered actual breaches
• Mechanisms vary (often ad-hoc)
• Encryption, user accounts (some people)
• Hide sensitive files in the file system
• Delete sensitive data so no one can see it
“If I didn’t want everyone to see them, I just had them
for a little while and then I just deleted them.”
http://www.pdl.cmu.edu/
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Michelle Mazurek © Oct 09
F2: Policy needs are complex
• Fine-grained divisions of people and files
strangers
teacher
boss
friends
parents
boyfriend
• Public, private aren’t enough
• More than friends, family, colleagues, strangers
• One policy:
shared
mixed
restricted
music
photos
private docs
study abroad docs
schoolwork
work files
other docs
http://www.pdl.cmu.edu/
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Michelle Mazurek © Oct 09
F2: Dimensions beyond person
• Read vs. write remains important
• Read-only is needed but not sufficient
• Presence resonated for most
• “If you have your mother in the room, you are not
going to do anything bad. But if your mom is
outside the room you can sneak.”
• Also can provide a chance to explain
• Location
• People in my home are trusted
• Higher level of “lockdown” when elsewhere
• Device, time of day not as popular
http://www.pdl.cmu.edu/
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Michelle Mazurek © Oct 09
F2: Variation across participants
• Finding reasonable defaults is difficult
• What is most/least private?
• Sharing-oriented vs. restriction-oriented
• “Basically, it’s my stuff; if I want you to have it, I’ll
give it to you.”
• “I don’t really have private files.... There’s nothing
that I am hiding from anybody.”
• Most have one “most trusted” person
• Definition of “most trusted” varies widely
http://www.pdl.cmu.edu/
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Michelle Mazurek © Oct 09
F3: Permission and control
• People like to be asked permission
• Positive response to reactive policy creation
• “I’m very willing to be open with people, I think I’d
just like the courtesy of someone asking me.”
• Setting policy ahead of doesn’t convey
control like being present and/or explicitly
granting permission
• If I’m present, “I can say, ‘These are the things
that you could see’.”
• “I can’t be giving you permission while I sleep
because I am sleeping.”
http://www.pdl.cmu.edu/
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Michelle Mazurek © Oct 09
F3: A-priori policy isn’t enough
• Last-minute decisions
• Review logs and fine-tune:
• “If someone has been looking at something a lot, I
am going to be a little suspicious. In general, I
would [then] restrict access to that specific file.”
• People want to know why as well as who
• “I might be worried about who else was watching.”
• “From my devices they would be able to view it but
not save it.”
http://www.pdl.cmu.edu/
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Michelle Mazurek © Oct 09
F4: Mental models ≠ systems
“If anything were to happen, ... I’m right there to say,
‘OK, what just happened?’ So I’m not as worried.”
• Desktop search finds “hidden” files
• Being present isn’t enough
• Violations can happen too fast to prevent
• Can’t necessarily monitor across the room
• Files can be shared across devices; files
within a device can be restricted
• “In my house” maybe not a good trust proxy
• Seems natural but fails after more thought
http://www.pdl.cmu.edu/
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Michelle Mazurek © Oct 09
Resulting design guidelines
• Allow fine-grained control
• Specification at multiple levels of granularity to
support varying needs
• Plan for lending devices
• Limited-access, discreet guest profiles1
• Include reactive policy creation
• “Sounds like the best possible scenario.”
• “It would be easy access for them while still
allowing me to control what they see.”
1Karlson
http://www.pdl.cmu.edu/
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et al., 2009
Michelle Mazurek © Oct 09
More design guidelines
• Reduce or eliminate up-front complexity
• “If I had to sit down and sort everything into what
people can view and cannot view, I think that
would annoy me. I wouldn’t do that.”
• Reactive policy creation can help with this
• Support iterative policy specification
• Interfaces designed to help users view/change
effective policy, not just rules
• Include human-readable logs
http://www.pdl.cmu.edu/
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Michelle Mazurek © Oct 09
Even more guidelines
• Acknowledge social conventions
• Requesting permission (reactive creation again)
• Plausible deniability: “I don’t want people to feel
that I am hiding things from them.”
• Account for users’ mental models
• A lot of mismatches come from incorrect analogies
to physical systems
• Either fit into existing models or explicitly guide
users to new mental models
http://www.pdl.cmu.edu/
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Michelle Mazurek © Oct 09
Access control in practice
• Smetters and Good: How Users Use Access
Control
• Collect usage history data from 200employee corporation
• Examine Windows and Unix user groups
• Examine e-mail lists
• Majority of document sharing is by e-mail
• Examine DocuShare usage
• File permissions
• Snapshots a year apart
Smetters and Good, 2009
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Analyzing groups
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Group name
List of members (users and groups)
Owner, create time, modify time
Who can update membership?
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Results – group membership
• Most groups have fewer than 20 members
• Users participate in more groups when groups
are user-defined (not administrators)
• Membership changes on order of months, years
System
Avg.
Users/Group
Avg.
Groups/User
Max Groups/User
Windows
6.3
3.7
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Unix/NFS
7.0
4.2
16
DocuShare 11.9
7.8
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Mailing lists 14.2
9.3
411
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Results – Group construction
• Users more often add members;
administrators more often “clean up”
• User-defined groups are more often
disorganized, duplicate
• Windows groups with clear structure, naming
convention
• DocuShare groups with overlapping and
misleading names
• Unix “emergency” groups with misspelled
usernames
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DocuShare settings
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List of ACLs to users or groups
Positive rules only (no deny rules)
Files have single owners with full rights
Policy inheritance: folder policy automatically
applies to documents added to that folder
• Inheritance prompt when folder settings change
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DocuShare data
• Collected files and folders visible to 8 users
• 49,672 unique objects (documents or folders)
• Consider how many users can see each item
Type
Percent
# Users # Items
File
72.7
1
3375
Folder
14.3
2
762
URL
6.7
3
250
Event
5.7
4
57
Calendar
0.2
5
1742
Bulletin board
0.1
6
40
7
877
8
42569
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Results – Setting permissions
• 5.2% of objects had permissions different
from their parent
• 3.5% of all documents; 15% of all folders
• Among changed items:
• 52% different list of principals
• 30% different permissions for a given principal
• 17% both
• Claim: In general users prefer to add files to
pre-set folder rather than set permissions
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Results – ACL contents
• Consider only
“explicitly set” lists
• 4527 user rules;
4755 group rules
• 22% read-only rules;
73% read-write rules
• Complex ACLs are
common
• Only 3.9% contain
exactly one group
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More about ACL contents
• Users are often double-listed: individually and
as part of a listed group
• All cases with 17 or more individuals
• Almost nothing is private to owner (8 docs)
• Lots of things are “public”
• 22% readable by built-in “everyone”
• 86% readable by local “authorized users”
• May be a function of DocuShare usage model,
and/or this organization’s open culture
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Discussion and guidelines
• Simplify access control
• Positive rules only
– Negative rules are complex and hard to parse
– Same goal can be achieved with better groups
• Simplify inheritance
– Don’t force users to choose
• Limit permission types
– Do we need anything beyond read, write, exec?
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Further guidelines
• Let users create their own groups
• Good groups make specifying good policy easier
• But earlier, disorganized groups are dangerous?
• Better management tools
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Group management: reduce redundancy
ACL management: promote groups over users
Cleanup management: prune outdated items
Intelligent activity-based grouping
Effective policy visualizations
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Overview of guidelines
• Multiple-precision policy configuration
• Statistical policy recommender systems
• Person and file clustering
• Location- and content-based
• To predict policy or warn about possible errors
• Make aggregate disclosure visible
• Provide comparison to social norms
• Show the user what others will see
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Overview of guidelines 2
• Decouple policy for different information types
• Search vs. access, photo vs. location, etc.
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Encourage users to get beyond defaults
Allow fine-grained control
Plan for lending devices
Include reactive policy creation
Reduce up-front complexity
Support iterative policy specification
Acknowledge social conventions
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Overview of guidelines 3
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Account for mental models
Positive rules only
Simplify policy inheritance
Limit permission types
Promote use of groups in access lists
• Let users create their own groups
• Tools to reduce redundancy and promote cleanup
• Activity-based suggestions
• Effective policy visualizations
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