Unlikely Outcomes? A Distributed Discussion on Decentralized Personal Data Architectures

March 27, 2013 at 7:44 am 1 comment

In recent years there has been a mushrooming of decentralized social networks, personal data stores and other such alternatives to the current paradigm of centralized services. In the academic paper A Critical Look at Decentralized Personal Data Architectures last year, my coauthors and I challenged the feasibility and desirability of these alternatives (I also gave a talk about this work). Based on the feedback, we realized it would be useful to explicate some of our assumptions and implicit viewpoints, add context to our work, clarify some points that were unclear, and engage with our critics on some of the more contentious claims.

We found the perfect opportunity to do this via an invitation from Unlike Us Reader, produced by the Institute of Network Cultures — it’s a magazine run by a humanities-oriented group of people, with a long-standing interest in digital culture, but they also attract some politically oriented developers. The Unlike Us conference, from which this edited volume stems, is also very interesting. [1]

Three of the five original authors — Solon, Vincent and I — teamed up with the inimitable Seda Gürses for an interview-style conversation (PDF). Seda is unique among privacy researchers — one of her interests is to understand and reconcile the often maddeningly divergent viewpoints of the different communities that study privacy, so she was the ideal person to play the role of interlocutor. Seda solicited feedback from about two dozen people in the hobbyist, activist and academic communities, and synthesized the responses into major themes. Then the three of us took turns responding to the prompts, which Solon, with Seda’s help, organized into a coherent whole. A majority of the commenters consented to making their feedback public, and Seda has collected the discussion into an online appendix.

This was an unusual opportunity, and I’m grateful to everyone who made it happen, particularly Seda and Solon who put in an immense amount of work. My participation was very enjoyable. Research proceeds at such a pace that we rarely have the opportunity to look back and cogitate about the process; when we do, we’re often surprised by what we find. For example, here’s something I noted with amusement in one of my responses:

My interest in decentralized social networking apparently dates to 2009, as I just discovered by digging through my archives. I’d signed up to give a talk on pitfalls of social networking privacy at a Stanford workshop, and while preparing for it I discovered the rich academic literature and the various hobbyist efforts in the decentralized model. My slides from that talk seem to anticipate several of the points we made about decentralized social networking in the paper (albeit in bullet-point form), along with the conclusion that they were “unlikely to disrupt walled gardens.” Funnily enough, I’d completely forgotten about having given this talk when we were writing the paper.

I would recommend reading this text as a companion to our original paper. Read it for extra context and clarifications, a discussion of controversial points, and as a way of stimulating thinking about the future prospects of alternative architectures. It may also be an interesting read as an example of how people writing an article together can have different views, and as a bit of a behind-the-scenes look at the research process.

[1] In particular, the latest edition of the conference that just concluded had a panel titled “Are you distributed? The Federated Web Show” moderated by Seda, with Vincent as one of the participants. It touched upon many of the same themes as our work.

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The job talk is a performance How I utilized “expectation failure” to refute privacy myths

1 Comment Add your own

  • 1. Cameron Lewis  |  April 8, 2013 at 3:39 pm

    I have long thought that a person behavioral data trading model, based on Board of Trade, where individuals are treated as producers (anonymous) & data types are treated as commodities (aggregate, longitudinal) and buyers could select data samples based on commodity types (cohorts) would be a great ecommerce opportunity & a way to get the most accurate empirical data into use.


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About 33bits.org

I'm an assistant professor of computer science at Princeton. I research (and teach) information privacy and security, and moonlight in technology policy.

This is a blog about my research on breaking data anonymization, and more broadly about information privacy, law and policy.

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