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After doing some research, we found that we can classify users based on their expertise in using software systems (to clear/update cookies in their browser, for example) and privacy concerns. Further categorization can be made depending on the type of data that users are interested in sharing (either for social or business purpose). With these classifications, we can divide users based on age in the following manner:

1. teen-2528: less aware, amount actually shared (greatest in social network), least (commercial data), as danah boyd puts it “they lack the agency to assert social norms and expect that others will respect them”. expertise (middle)
   1. Summary: less privacy concerns, medium expertise, most amount of social data, least amount of commercial/business data,
2. 2528-5560: more likely to share data with businesses, more proactive in refusing to share data, most expertise (in actively deleting cookies etc), more likely to have personal negative experiences
   1. Summary: medium privacy concerns, most expertise, medium amount of social data, most amount of commercial/business data,

3. Wiki Markup
   <span style="color: #000000">55>60+:</span> most conservative, have greatest privacy concerns, least amount of technical expertise,&nbsp; least likely to share data. Interestingly, \[1\] describes how none of their study participants expressed a need to create new acquaintances on the Web.

   1. Summary: most privacy concerns, least expertise, least amount of social data, medium amount of commercial/business data,

This can be viewed graphically as follows:

We performed contextual inquiry For the purpose of the user study, we performed contextual inquiry on the first two user groups (teen-30 and 30-60), since they are the ones that are the more likely to share both types of data (social and business-related). We sought to learn more about how people use existing systems, and what features they might like to see in a location-sharing app. In particular, we inquired about social and commercial uses of location-data sharing. We interviewed people who represented

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The goal of this task is to review and perhaps change the type of data the user is sharing with others. The precondition is that one or more relationships have been previously established, and the desire to review or edit is generated either by curiosity, or by some recent behavior that a user might want to share or not share in some existing relationship. Because the user can see all of his/her own data here, the task may be performed multiple times per week. We expect people to be curious about their own data and how it appears to others. This activity can be learned by doing -- it just involves scrolling, clicking, zooming, and panning. Viewing is inherently very safe, and editing can be made more safe by including the option to undo actions. 

References:

boyd, danah and Marwick, Alice E., Social Privacy in Networked Publics: Teens’ Attitudes, Practices, and Strategies (September 22, 2011). A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society, September 2011. Available at SSRN: http://ssrn.com/abstract=1925128^{Image Added}

Hoofnagle, Chris Jay, King, Jennifer, Li, Su and Turow, Joseph, How Different are Young Adults from Older Adults When it Comes to Information Privacy Attitudes and Policies? (April 14, 2010). Available at SSRN: http://ssrn.com/abstract=1589864^{Image Added} or http://dx.doi.org/10.2139/ssrn.1589864^{Image Added}

Vilma Lehtinen, Jaana Näsänen, and Risto Sarvas. 2009. "A little silly and empty-headed": older adults' understandings of social networking sites. In Proceedings of the 23rd British HCI Group Annual Conference on People and Computers: Celebrating People and Technology (BCS-HCI '09). British Computer Society, Swinton, UK, UK, 45-54.

Johann Schrammel, Christina Köffel, and Manfred Tscheligi. 2009. How much do you tell?: information disclosure behaviour indifferent types of online communities. In Proceedings of the fourth international conference on Communities and technologies (C&T '09). ACM, New York, NY, USA, 275-284. DOI=10.1145/1556460.1556500 http://doi.acm.org/10.1145/1556460.1556500^{Image Added}

TA Feedback.

This is a good start, but there are some big things missing. You don't actually discuss classes of users, just specific interviewees. Try to think about who your users are more holistically - right now they're sort of points along a 1D continuum.

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