- Distinguishing Cause and Effect using Observational Data — research paper evaluating effectiveness of the “additive noise” test, a nifty statistical trick to identify causal relationships from observational data. (via Slashdot)
- Clustering Bitcoin Accounts Using Heuristics (O’Reilly Radar) — In theory, a user can go by many different pseudonyms. If that user is careful and keeps the activity of those different pseudonyms separate, completely distinct from one another, then they can really maintain a level of, maybe not anonymity, but again, cryptographically it’s called pseudo-anonymity. […] It turns out in reality, though, the way most users and services are using bitcoin, was really not following any of the guidelines that you would need to follow in order to achieve this notion of pseudo-anonymity. So, basically, what we were able to do is develop certain heuristics for clustering together different public keys, or different pseudonyms.
- A Primer on Hardware Security: Models, Methods, and Metrics (PDF) — Camouflaging: This is a layout-level technique to hamper image-processing-based extraction of gate-level netlist. In one embodiment of camouflaging, the layouts of standard cells are designed to look alike, resulting in incorrect extraction of the netlist. The layout of nand cell and the layout of nor cell look different and hence their functionality can be extracted. However, the layout of a camouflaged nand cell and the layout of camouflaged nor cell can be made to look identical and hence an attacker cannot unambiguously extract their functionality.
- Prompter: A Domain-Specific Language for Versu (PDF) — literally a scripting language (you write theatrical-style scripts, characters, dialogues, and events) for an inference engine that lets you talk to characters and have a different story play out each time.
Lessons from the design community for developing data-driven applications
When you hear someone say, “that is a nice infographic” or “check out this sweet dashboard,” many people infer that they are “well-designed.” Creating accessible (or for the cynical, “pretty”) content is only part of what makes good design powerful. The design process is geared toward solving specific problems. This process has been formalized in many ways (e.g., IDEO’s Human Centered Design, Marc Hassenzahl’s User Experience Design, or Braden Kowitz’s Story-Centered Design), but the basic idea is that you have to explore the breadth of the possible before you can isolate truly innovative ideas. We, at Datascope Analytics, argue that the same is true of designing effective data science tools, dashboards, engines, etc — in order to design effective dashboards, you must know what is possible.