Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 1 Design for Privacy 1 February.

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Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 1 Design for Privacy 1 February 28, 2006

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 2 Outline Brief overview and key points from readings Privacy issues and human-computer interaction A user-centric privacy space framework Design of privacy tools Design for privacy in everyday software Your turn

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 3 Chapter 19: Privacy Issues and Human-Computer Interaction Mark S. Ackerman and Scott D. Mainwaring

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 4 What is privacy? Many definitions From and HCI perspective: “Privacy is about individuals’ capabilities in a particular situation to control what they consider to be personal data.” Key ideas relevant to HCI: Control Risk perception and risk management Ethical, political, and legal issues often need to be addressed when considering privacy Individual and context dependent

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 5 Usability engineering for privacy Similar to usability engineering for any other type of design problem, BUT Privacy not the a primary task Individualized privacy needs Privacy failures can be dangerous There may be legal requirements related to privacy

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 6 Privacy and CSCW Initially CSCW work ignored privacy because it was assumed that collaboration did not raise privacy issues In 1990s CSCW researchers started to realize that their work raised lots of privacy issues Privacy-related CSCW research Media space applications Other collaborative applications with privacy concerns Privacy and awareness

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 7 Individual differences w/r/t privacy Privacy differences People have differing types of concerns People also differ in their level of concern Approaches to addressing these differences Better interfaces Clustering users Adaptive systems Systems for training users User-tailorable systems

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 8 Ubicomp privacy Sensors create privacy concerns Privacy guidelines can help mitigate concerns Important to design systems that limit flow of information and allow for access controls

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 9 Chapter 20: A User-Centric Privacy Space Framework Benjamin Brunk

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 10 Exoinformation Information we shed as we go about our normal activities Exoinformation = data shadow = data exhaust

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 11 Privacy Space Framework Awareness Detection Prevention Response Recovery Brunk, Figure 20-2 p. 414

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 12 Design of Privacy Tools

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 13 Privacy tool examples Cookie managers Anonymizers Encryption tools Disk wiping utilities P3P user agents

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 14 Issues to consider Privacy is a secondary task Users of privacy tools often seek out these tools due to their awareness of or concern about privacy Even so, users still want to focus on their primary tasks Users have differing privacy concerns and needs One-size-fits-all interface may not work Most users are not privacy experts Difficult to explain current privacy state or future privacy implications Difficult to explain privacy options to them Difficult to capture privacy needs/preferences Many privacy tools reduce application performance, functionality, or convenience

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 15 Case study: Tor Internet anonymity system Allows users to send messages that cannot be traced back to them (web browsing, chat, p2p, etc.) UI was mostly command line interface until recently 2005 Tor GUI competition CUPS team won phase 1 with design for Foxtor!

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 16 One-size-doesn’t-fit-all problem Tor is configurable and different users will want to configure it in different ways But most users won’t understand configuration options Give users choices, not dilemmas We began by trying to understand our users No budget, little time, limited access to users So we brainstormed about their needs, tried to imagine them, and develop personas for them This process led to realization that our users had 3 categories of privacy needs Basic, selective, critical Instead of asking users to figure out complicated settings, most of our configuration involves figuring out which types of privacy needs they have

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 17

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 18 Understand primary task Anonymity is not a primary task What are the primary tasks our users are engaged in when they want anonymity? Lots of them …. Web browsing, chatting, file sharing, etc., but we speculate that browsing will be most frequent for most users So, instead of building anonymity tool that you can use to anonymize web browsing… … build a web browser with built in anonymity functions

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 19 Metaphors Because of performance issues and problems accessing some web sites through Tor, some users will want to turn the anonymity function on and off Important to make it easy for users to determine current state Communicate through visual symbol and readily understandable metaphor Brainstormed possibilities: torized/untorized, private/exposed, cloaked/uncloaked, masked/unmasked

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 20

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 21 Next steps Build or prototype User studies

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 22 Design for privacy in every day software

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 23 Examples Ecommerce personalization systems Concerns about use of user profiles Software that “phones home” to fetch software updates or refresh content, report bugs, relay usage data, verify authorization keys, etc. Concerns that software will track and profile users Communications software ( , IM, chat) Concerns about traffic monitoring, eavesdroppers Presence systems (buddy lists, shared spaces, friend finders) Concerns about limiting when info is shared and with whom

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 24 Issues to consider Similar to issues to consider for privacy tools PLUS Users may not be aware of privacy issues up front When they find out about privacy issues they may be angry or confused, especially if they view notice as inadequate or defaults as unreasonable Users may have to give up functionality or convenience, or spend more time configuring system for better privacy Failure to address privacy issues adequately may lead to bad press and legal action

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 25 Amazon.com privacy makeover

Streamline menu navigation for customization

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 27 Provide way to set up default rules Every time a user makes a new purchase that they want to rate or exclude they have to edit profile info There should be a way to set up default rules  Exclude all purchases  Exclude all purchases shipped to my work address  Exclude all movie purchases  Exclude all purchases I had gift wrapped

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 28 Remove excluded purchases from profile Users should be able to remove items from profile If purchase records are needed for legal reasons, users should be able to request that they not be accessible online

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 29 Better: options for controlling recent history

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 30 Use personae Amazon already allows users to store multiple credit cards and addresses Why not allow users to create personae linked to each with option of keeping recommendations and history separate (would allow easy way to separate work/home/gift personae)?

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 31 Allow users to access all privacy- related options in one place Currently privacy-related options are found with relevant features Users have to be aware of features to find the options Put them all in one place But also leave them with relevant features

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 32 I didn’t buy it for myself How about an “I didn’t buy it for myself” check- off box (perhaps automatically checked if gift wrapping is requested) I didn’t buy it for myself

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 33 Other ideas for improving Amazon privacy interface?

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 34 Your turn

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 35 Group problems Informing users about and configuring phone home features Configuring release of location information in friend finder service

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 36 Phone home features Many software products contain phone home features, for example, for performing software updates or monitoring usage patterns. In some cases software phones homes quite frequently, for example, to update phishing black lists or check for fresh image files. Users may be concerned that the software company is using these features to track or profile them. Thus it is important that the software is up front about the fact that it is phoning home. Furthermore, some users may wish to disable such features or be prompted every time before they phone home (due to privacy or other concerns), whereas other users are happy to have them operate automatically. Discuss the various approaches you have seen different software manufacturers take to addressing this problem. What do you like/dislike about them? How should phone home features be designed so that they facilitate informed consent? Describe an example user interface design and general principles that might be applied to specific cases. What sort of user studies should be performed to test this user interface design?

Usable Privacy and Security Carnegie Mellon University Spring 2006 Cranor/Hong/Reiter 37 Configuring a friend finder New location-based services are becoming available that allow individuals to use their cell phones to keep track of the location of their friends (or kids or employees, etc.). Imagine a service that allows users to configure their phones to automatically provide their location information to some people but not others under certain conditions. For example, location disclosure might be limited depending on time of day, the user’s location, or the person requesting the location. Design a configuration interface to allow a user to setup the conditions under which their location will be disclosed. How will you deal with the small phone screen? What are the basic primitives that will be needed for configuration rules? How will you make it easy for users to manage large numbers of rules and understand the consequences of adding or removing rules? What kinds of user studies would you perform to test such an interface?