It was Dick Hardt who got me interested in user-centric identity with his great presentation on identity 2.0. It was funny, wise and asked some very intriguing questions. Dick recently announced he was pulling the plug on Sxipper, his ground-breaking “identity” product, that operated as a Firefox browser plugin. Dick doesn’t much like to use the word “identity” when discussing “identity.” He prefers to talk about identifers, the tokens we trade in authentication, authorization and other kinds of networked transactions. As a veteran of the Internet Identity Workshops, he knew that the word “identity” is overdetermined and tends to overflow the kind of boundaries required for productive technical discussions.
The difficulty of user-centric systems has always been the contradiction at their core. The user doesn’t own the technical infrastructure to support an identity system, so systems that pretended to be outside of the system of systems provided the third leg of the triangle of “user-centric” authentication. This system multiplied the number of players in the game—supposedly in order to shift the balance of power back to the user. From the user’s perspective it merely complicated something that was too complicated to begin with.
Sxipper took a different approach, one that is gaining some popularity now in the form of the personal data locker. Sxipper looked at every form a user encountered on the web as an opportunity to learn something new. If Sxipper already understood a form, it would ask you how you wanted it filled out. If you’d populated your persona bank, you could select the appropriate data set, and Sxipper would automatically fill out the form for you. Once you’d trained Sxipper to understand a form, you were all set. Sxipper users also benefitted from the community of users, if someone else had trained the form, you were also ready to go. Web transaction forms used by large populations were almost always already trained, at the margins you’d have to do the training yourself. But rather than assume all forms at a Network level (commercial transaction, web site sign up, authentication, etc) needed to be accounted for, Sxipper focused by design, on what people actually did. Translating transaction difference in the trenches. As you used Sxipper, the amount of transaction friction you experienced on the web was continuously reduced.
With all this personal data and preference information in a persona bank, you might get the idea that your data might be worth something—that you could trade it for valuable gifts, discounts and prizes. While this model does work, it only works for celebrities. The network celebrity hub with high numbers of links gain even more links through the phenomena of preferential attachment. Value flows to these hubs by virtue of their potential distribution power. For example, in Hollywood, if a star can ‘open a movie,’ they’re compensated for it.
The big networked systems derive value from their scale and the correlation data they unearth from the big data they custody. The patterns they produce through statistical analysis can be sold under various schemes. Primarily, target groups are sold to advertisers. For most members of the target set, their data is commodity. Subtract a member from the set and the pattern remains. Your personal data only has value in concert with all the other members who make up the set. As a single point on a graph, your data doesn’t describe a trend.
With all this data flying around, it would seem that personalization of user experiences would naturally follow. And to some extent, a form of this is happening, but it’s through common patterns, not through deep insight into personal data. Augmented reality (the normalization of reference delusions) attempts to personalize the physical space you move through by superimposing targeted advertising-sponsored hypermedia publications on the smear of spatio-temporal location coordinates surrounding you. Reality becomes shelf space, with brands fighting the visual merchandising war for a home in your selection set.
Back to Sxipper: In order to provide personal data from a persona bank to domesticated web forms and transaction interfaces, Sxipper had to sit in a particular spot on the Network. As a browser plugin, or App, as we call them these days, Sxipper could send and receive form training data to a central cloud; and combine that formal data with a user’s locally-stored encrypted personal data to fill in forms across many different sites. Rather than harvesting correlation data, Sxipper had no access to it’s user’s personal data stores; because of this it had no target audiences to sell.
The value of identity and gesture data has been an ongoing discussion in the internet identity community. It seems like there must be a business model in there somewhere. The digital deal, the gesture bank, the attention economy, root markets, vendor relationship management, and now personal data lockers have all explored the system (bank) and account model. Anonymized central bank data can still yield correlation data, the patterns, but it forgoes the regular distribution model except through a user opt in. It’s a business model that makes sense and honors user privacy, but has yet to be successfully implemented.
The Selector, along with the information and action card had a similar, but more general structure, as Sxipper. Essentially, it was a client-side application development environment. Information cards were the equivalent of personal data personas, and action cards extended the capability to run a much wider variety of personal scripts across data from multiple sources. But like Sxipper, Microsoft recently put the final nail in the Selector and information card.
There are a couple of ideas from Sxipper that I’d like to see survive its demise. The first is focusing on difference rather than identity. Sxipper used the crowd to build bridges between different interfaces and systems. The diversity of the Network environment is one of its strengths. Sxipper preserved the diversity, but made the complexity disappear. The other idea is, rather than generating personalization from a central data bank, create personalization from the user’s side of the glass. Think of personalization as emanating from the person, rather than the system.
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